{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T23:52:11Z","timestamp":1775692331274,"version":"3.50.1"},"reference-count":229,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HARMONIA project 2020-LC-CLA-2018-2019-2020","award":["101003517"],"award-info":[{"award-number":["101003517"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the increase in climate-change-related hazardous events alongside population concentration in urban centres, it is important to provide resilient cities with tools for understanding and eventually preparing for such events. Machine learning (ML) and deep learning (DL) techniques have increasingly been employed to model susceptibility of hazardous events. This study consists of a systematic review of the ML\/DL techniques applied to model the susceptibility of air pollution, urban heat islands, floods, and landslides, with the aim of providing a comprehensive source of reference both for techniques and modelling approaches. A total of 1454 articles published between 2020 and 2023 were systematically selected from the Scopus and Web of Science search engines based on search queries and selection criteria. ML\/DL techniques were extracted from the selected articles and categorised using ad hoc classification. Consequently, a general approach for modelling the susceptibility of hazardous events was consolidated, covering the data preprocessing, feature selection, modelling, model interpretation, and susceptibility map validation, along with examples of related global\/continental data. The most frequently employed techniques across various hazards include random forest, artificial neural networks, and support vector machines. This review also provides, per hazard, the definition, data requirements, and insights into the ML\/DL techniques used, including examples of both state-of-the-art and novel modelling approaches.<\/jats:p>","DOI":"10.3390\/rs16183374","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T05:45:12Z","timestamp":1726033512000},"page":"3374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9500-6314","authenticated-orcid":false,"given":"Angelly de Jesus","family":"Pugliese Viloria","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy"}]},{"given":"Andrea","family":"Folini","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1262-9394","authenticated-orcid":false,"given":"Daniela","family":"Carrion","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3161-5561","authenticated-orcid":false,"given":"Maria Antonia","family":"Brovelli","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci, 32, 20133 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"ref_1","unstructured":"IPCC (2023). IPCC, 2023: Summary for Policymakers. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1016\/j.scitotenv.2016.07.008","article-title":"Global assessment of heat wave magnitudes from 1901 to 2010 and implications for the river discharge of the Alps","volume":"571","author":"Zampieri","year":"2016","journal-title":"Sci. Total Environ."},{"key":"ref_3","unstructured":"Munich Re (2022). Hurricanes, Cold Waves, Tornadoes: Weather Disasters in USA Dominate Natural Disaster Losses in 2021, Munich Re."},{"key":"ref_4","unstructured":"Asian Development Bank (2013). Moving from Risk to Resilience: Sustainable Urban Development in the Pacific, Asian Development Bank."},{"key":"ref_5","first-page":"209","article-title":"Recommendations for the quantitative analysis of landslide risk","volume":"73","author":"Corominas","year":"2014","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1007\/s11069-011-9721-z","article-title":"Quantitative multi-risk analysis for natural hazards: A framework for multi-risk modelling","volume":"58","author":"Schmidt","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103752","DOI":"10.1016\/j.earscirev.2021.103752","article-title":"Exploring machine learning potential for climate change risk assessment","volume":"220","author":"Zennaro","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.bdr.2015.01.003","article-title":"Geospatial Big Data: Challenges and Opportunities","volume":"2","author":"Lee","year":"2015","journal-title":"Big Data Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"134656","DOI":"10.1016\/j.jclepro.2022.134656","article-title":"Predicting the quality of air with machine learning approaches: Current research priorities and future perspectives","volume":"379","author":"Mehmood","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.enggeo.2008.03.022","article-title":"Guidelines for landslide susceptibility, hazard and risk zoning for land use planning","volume":"102","author":"Fell","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.5194\/hess-26-4345-2022","article-title":"Deep learning methods for flood mapping: A review of existing applications and future research directions","volume":"26","author":"Bentivoglio","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nhu, V.H., Zandi, D., Shahabi, H., Chapi, K., Shirzadi, A., Al-Ansari, N., Singh, S.K., Dou, J., and Nguyen, H. (2020). Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran. Appl. Sci., 10.","DOI":"10.3390\/app10155047"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.proeps.2014.06.006","article-title":"Integrated Physically based System for Modeling Landslide Susceptibility","volume":"9","author":"Formetta","year":"2014","journal-title":"Procedia Earth Planet. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Feng, L., Guo, M., Wang, W., Chen, Y., Shi, Q., Guo, W., Lou, Y., Kang, H., Chen, Z., and Zhu, Y. (2022). Comparative Analysis of Machine Learning Methods and a Physical Model for Shallow Landslide Risk Modeling. Sustainability, 15.","DOI":"10.3390\/su15010006"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1007\/s11069-022-05423-7","article-title":"Machine learning and landslide studies: Recent advances and applications","volume":"114","author":"Tehrani","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.gsf.2020.09.002","article-title":"Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks","volume":"12","author":"Pradhan","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, Q., Ren, M., Wu, S., Sun, Y., Wang, J., Wang, Q., Ma, Y., Song, X., and Chen, Y. (2022). Applications of artificial intelligence in the field of air pollution: A bibliometric analysis. Front. Public Health, 10.","DOI":"10.3389\/fpubh.2022.933665"},{"key":"ref_20","unstructured":"Pugliese-Viloria, A. (2024, July 29). Hazard Susceptibility Mapping with Machine and Deep Learning: A Literature Review\u2014Data and Software, V1.0.0; Zenodo: 2024. Available online: https:\/\/zenodo.org\/records\/13386422."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s12525-021-00475-2","article-title":"Machine learning and deep learning","volume":"31","author":"Janiesch","year":"2021","journal-title":"Electron. Mark."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chauhan, N.K., and Singh, K. (2018, January 28\u201329). A review on conventional machine learning vs. deep learning. Proceedings of the 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018, Greater Noida, Uttar Pradesh, India.","DOI":"10.1109\/GUCON.2018.8675097"},{"key":"ref_23","unstructured":"Patgiri, R. (2018). Taxonomy of Big Data: A Survey. arXiv."},{"key":"ref_24","first-page":"778","article-title":"A review on Machine Learning Techniques","volume":"8","author":"Preeti","year":"2017","journal-title":"Int. J. Adv. Res. Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1097\/MEG.0b013e3282f198a0","article-title":"Introduction to artificial neural networks","volume":"19","author":"Grossi","year":"2008","journal-title":"Eur. J. Gastroenterol. Hepatol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nikparvar, B., and Thill, J.C. (2021). Machine Learning of Spatial Data. ISPRS Int. J. -Geo-Inf., 10.","DOI":"10.3390\/ijgi10090600"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1109\/TKDE.2019.2954510","article-title":"Deep Air Quality Forecasting Using Hybrid Deep Learning Framework","volume":"33","author":"Du","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"114513","DOI":"10.1016\/j.eswa.2020.114513","article-title":"Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering","volume":"169","author":"Yan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"26933","DOI":"10.1109\/ACCESS.2020.2971348","article-title":"A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5)","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","first-page":"1","article-title":"Attention is All you Need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yu, H., Pei, W., Zhang, J., and Chen, G. (2023). Landslide Susceptibility Mapping and Driving Mechanisms in a Vulnerable Region Based on Multiple Machine Learning Models. Remote Sens., 15.","DOI":"10.3390\/rs15071886"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"and Vastrad, C.M. (2013). Performance Analysis Of Regularized Linear Regression Models For Oxazolines And Oxazoles Derivitive Descriptor Dataset. arXiv.","DOI":"10.5121\/ijist.2013.3601"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/00220670209598786","article-title":"An Introduction to Logistic Regression Analysis and Reporting","volume":"96","author":"Peng","year":"2002","journal-title":"J. Educ. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106620","DOI":"10.1016\/j.ecolind.2020.106620","article-title":"GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, na\u00efve Bayes tree, bivariate statistics and logistic regression: A case of Top\u013ea basin, Slovakia","volume":"117","author":"Ali","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of Decision Trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_36","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Chapman and Hall\/CRC."},{"key":"ref_37","unstructured":"Quinlan, J. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann."},{"key":"ref_38","unstructured":"Elomaa, T., and K\u00e4\u00e4ri\u00e4inen, M. (2011). An Analysis of Reduced Error Pruning. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_40","first-page":"31","article-title":"Support Vector Machines and Kernel Methods: The New Generation of Learning Machines","volume":"23","author":"Cristianini","year":"2002","journal-title":"AI Mag."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"134514","DOI":"10.1016\/j.scitotenv.2019.134514","article-title":"Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models","volume":"711","author":"Costache","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1016\/j.scitotenv.2018.10.064","article-title":"An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines","volume":"651","author":"Choubin","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104426","DOI":"10.1016\/j.catena.2019.104426","article-title":"Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment","volume":"188","author":"Bui","year":"2020","journal-title":"CATENA"},{"key":"ref_46","unstructured":"Freund, Y., and Schapire, R.E. (August, January 31). A Short Introduction to Boosting. Proceedings of the 16th International Joint Conference on Artificial Intelligence, IJCAI\u201999, Stockholm, Sweden."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 16, New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_51","first-page":"102932","article-title":"Applications of Stacking\/Blending ensemble learning approaches for evaluating flash flood susceptibility","volume":"112","author":"Yao","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/BF00153759","article-title":"Instance-Based Learning Algorithms","volume":"6","author":"Aha","year":"1991","journal-title":"Mach. Learn."},{"key":"ref_53","first-page":"1","article-title":"k-Nearest neighbour classifiers","volume":"54","author":"Cunningham","year":"2007","journal-title":"Mult. Classif. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Adnan, M.S.G., Rahman, M.S., Ahmed, N., Ahmed, B., Rabbi, M.F., and Rahman, R.M. (2020). Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12203347"},{"key":"ref_55","unstructured":"Zhu, J., Chen, J., and Hu, W. (2014). Big Learning with Bayesian Methods. arXiv."},{"key":"ref_56","unstructured":"Vijaykumar, B. (2014). Bayes and Naive Bayes Classifier. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"91896","DOI":"10.1109\/ACCESS.2021.3091162","article-title":"Forecast Methods for Time Series Data: A Survey","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at Scale","volume":"72","author":"Taylor","year":"2017","journal-title":"Am. Stat."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1007\/s40747-021-00637-x","article-title":"Feature dimensionality reduction: A review","volume":"8","author":"Jia","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, D., Wen, H., Zhang, H., and Zhang, F. (2020). Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China). Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17124206"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s40745-015-0040-1","article-title":"A comprehensive survey of clustering algorithms","volume":"2","author":"Xu","year":"2015","journal-title":"Ann. Data Sci."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Lin, J., Sreng, C., Oare, E., and Batarseh, F.A. (2023). NeuralFlood: An AI-driven flood susceptibility index. Front. Water, 5.","DOI":"10.3389\/frwa.2023.1291305"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1007\/s10346-020-01473-9","article-title":"Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1007\/s11629-020-6491-7","article-title":"Assessment of landslide susceptibility using DBSCAN-AHD and LD-EV methods","volume":"19","author":"Mao","year":"2022","journal-title":"J. Mt. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/S0952-1976(00)00020-8","article-title":"An implementation of genetic algorithms for rule based machine learning","volume":"13","author":"Sette","year":"2000","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"121975","DOI":"10.1016\/j.jclepro.2020.121975","article-title":"High temporal resolution prediction of street-level PM2.5 and NOx concentrations using machine learning approach","volume":"268","author":"Li","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1126\/science.153.3731.34","article-title":"Dynamic programming","volume":"153","author":"Bellman","year":"1966","journal-title":"Science"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3561048","article-title":"Explainable AI (XAI): Core Ideas, Techniques, and Solutions","volume":"55","author":"Dwivedi","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.compeleceng.2013.11.024","article-title":"A survey on feature selection methods","volume":"40","author":"Chandrashekar","year":"2014","journal-title":"Comput. Electr. Eng."},{"key":"ref_70","first-page":"3","article-title":"A review of Feature Selection and its methods","volume":"19","author":"Venkatesh","year":"2019","journal-title":"Cybern. Inf. Technol."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Dosilovic, F.K., Brcic, M., and Hlupic, N. (2018, January 21\u201325). Explainable artificial intelligence: A survey. Proceedings of the 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018\u2014Proceedings, Opatija, Croatia.","DOI":"10.23919\/MIPRO.2018.8400040"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.jes.2021.12.035","article-title":"Deciphering urban traffic impacts on air quality by deep learning and emission inventory","volume":"124","author":"Du","year":"2023","journal-title":"J. Environ. Sci."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"117230","DOI":"10.1016\/j.eswa.2022.117230","article-title":"When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates","volume":"202","author":"Henckaerts","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"120111","DOI":"10.1016\/j.atmosenv.2023.120111","article-title":"Estimation of daily NO2 with explainable machine learning model in China, 2007\u20132020","volume":"314","author":"Shao","year":"2023","journal-title":"Atmos. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"101605","DOI":"10.1016\/j.uclim.2023.101605","article-title":"Urban effects on precipitation: Do the diversity of research strategies and urban characteristics preclude general conclusions?","volume":"51","author":"Lalonde","year":"2023","journal-title":"Urban Clim."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"115736","DOI":"10.1016\/j.envpol.2020.115736","article-title":"Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model","volume":"268","author":"Nabavi","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.gr.2022.07.013","article-title":"Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization","volume":"123","author":"Sun","year":"2022","journal-title":"Gondwana Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.dss.2011.01.013","article-title":"Performance of classification models from a user perspective","volume":"51","author":"Martens","year":"2011","journal-title":"Decis. Support Syst."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"134474","DOI":"10.1016\/j.scitotenv.2019.134474","article-title":"Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain","volume":"701","author":"Choubin","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"134413","DOI":"10.1016\/j.scitotenv.2019.134413","article-title":"A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area","volume":"701","author":"Bui","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Lei, T.M.T., Ng, S.C.W., and Siu, S.W.I. (2023). Application of ANN, XGBoost, and Other ML Methods to Forecast Air Quality in Macau. Sustainability, 15.","DOI":"10.3390\/su15065341"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Pourghasemi, H.R., Kariminejad, N., Amiri, M., Edalat, M., Zarafshar, M., Blaschke, T., and Cerda, A. (2020). Assessing and mapping multi-hazard risk susceptibility using a machine learning technique. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-60191-3"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.nhres.2022.06.003","article-title":"Flood susceptibility zonation using advanced ensemble machine learning models within Himalayan foreland basin","volume":"2","author":"Ghosh","year":"2022","journal-title":"Nat. Hazards Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"124379","DOI":"10.1016\/j.jhydrol.2019.124379","article-title":"Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping","volume":"581","author":"Bui","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1007\/s11069-022-05793-y","article-title":"Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations","volume":"116","author":"Aydin","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1007\/s00477-023-02507-z","article-title":"Flood susceptibility mapping with ensemble machine learning: A case of Eastern Mediterranean basin, Turkiye","volume":"37","author":"Ozdemir","year":"2023","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1007\/s13753-023-00477-y","article-title":"A Hybrid Multi-Hazard Susceptibility Assessment Model for a Basin in Elazig Province, Turkiye","volume":"14","author":"Karakas","year":"2023","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1016\/j.gsf.2019.10.008","article-title":"Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?","volume":"11","author":"Pourghasemi","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Yousefi, S., Pourghasemi, H.R., Emami, S.N., Pouyan, S., Eskandari, S., and Tiefenbacher, J.P. (2020). A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-69233-2"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"152836","DOI":"10.1016\/j.scitotenv.2021.152836","article-title":"Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches","volume":"815","author":"Oukawa","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Zhang, X., Huang, T., Gulakhmadov, A., Song, Y., Gu, X., Zeng, J., Huang, S., Nam, W.H., Chen, N., and Niyogi, D. (2022). Deep Learning-Based 500 m Spatio-Temporally Continuous Air Temperature Generation by Fusing Multi-Source Data. Remote Sens., 14.","DOI":"10.3390\/rs14153536"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"5074","DOI":"10.1109\/JSTARS.2020.3019696","article-title":"Summer Nights in Berlin, Germany: Modeling Air Temperature Spatially With Remote Sensing, Crowdsourced Weather Data, and Machine Learning","volume":"13","author":"Vulova","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"109996","DOI":"10.1016\/j.knosys.2022.109996","article-title":"Ambient air pollutants concentration prediction during the COVID-19: A method based on transfer learning","volume":"258","author":"Chen","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"150130","DOI":"10.1016\/j.scitotenv.2021.150130","article-title":"An urban energy balance-guided machine learning approach for synthetic nocturnal surface Urban Heat Island prediction: A heatwave event in Naples","volume":"805","author":"Oliveira","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_95","first-page":"102066","article-title":"Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data","volume":"88","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"101116","DOI":"10.1016\/j.uclim.2022.101116","article-title":"Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India","volume":"42","author":"Mohammad","year":"2022","journal-title":"Urban Clim."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Wang, Y., Liang, Z., Ding, J., Shen, J., Wei, F., and Li, S. (2022). Prediction of Urban Thermal Environment Based on Multi-Dimensional Nature and Urban Form Factors. Atmosphere, 13.","DOI":"10.3390\/atmos13091493"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Avand, M., Janizadeh, S., Phong, T.V., Al-Ansari, N., Ho, L.S., Das, S., Le, H.V., Amini, A., and Bozchaloei, S.K. (2020). GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. Water, 12.","DOI":"10.3390\/w12030683"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Nhu, V.H., Ngo, P.T.T., Pham, T.D., Dou, J., Song, X., Hoang, N.D., Tran, D.A., Cao, D.P., Aydilek, \u0130.B., and Amiri, M. (2020). A New Hybrid Firefly\u2013PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12172688"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"101075","DOI":"10.1016\/j.gsf.2020.09.006","article-title":"Flood susceptibility modelling using advanced ensemble machine learning models","volume":"12","author":"Islam","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"104107","DOI":"10.1016\/j.scs.2022.104107","article-title":"Modelling the impacts of land use\/land cover changing pattern on urban thermal characteristics in Kuwait","volume":"86","author":"AlDousari","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"104307","DOI":"10.1016\/j.scs.2022.104307","article-title":"Urban flood susceptibility mapping based on social media data in Chengdu city, China","volume":"88","author":"Li","year":"2023","journal-title":"Sustain. Cities Soc."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"He, W., Zhang, S., Meng, H., Han, J., Zhou, G., Song, H., Zhou, S., and Zheng, H. (2022). Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach. Remote Sens., 14.","DOI":"10.3390\/rs14153571"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"159087","DOI":"10.1016\/j.scitotenv.2022.159087","article-title":"Resilient landscape pattern for reducing coastal flood susceptibility","volume":"856","author":"Luo","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"107485","DOI":"10.1016\/j.envint.2022.107485","article-title":"Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression","volume":"168","author":"Shen","year":"2022","journal-title":"Environ. Int."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"93","DOI":"10.2478\/geosc-2022-0008","article-title":"Using open data to reveal factors of urban susceptibility to natural hazards and human-made hazards: Case of Milan and Sofia","volume":"16","author":"Vavassori","year":"2022","journal-title":"GeoScape"},{"key":"ref_107","unstructured":"Environmental Pollution Centers (2022). What Is Air Pollution, Environmental Pollution Centers."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Kim, H., and Park, H. (2022). Leveraging Machine Learning for Fault-Tolerant Air Pollutants Monitoring for a Smart City Design. Electronics, 11.","DOI":"10.3390\/electronics11193122"},{"key":"ref_109","unstructured":"European Environment Agency (2021). European Air Quality Index, European Environment Agency."},{"key":"ref_110","first-page":"6103","article-title":"Environmental hazard assessment and monitoring for air pollution using machine learning and remote sensing","volume":"20","author":"Soliman","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"117859","DOI":"10.1016\/j.envpol.2021.117859","article-title":"Spatio-temporal modeling of PM2.5 risk mapping using three machine learning algorithms","volume":"289","author":"Shogrkhodaei","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.5194\/acp-20-3273-2020","article-title":"Improved 1km resolution PM2.5 estimates across China using enhanced space\u2013time extremely randomized trees","volume":"20","author":"Wei","year":"2020","journal-title":"Atmos. Chem. Phys."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"105297","DOI":"10.1016\/j.envint.2019.105297","article-title":"Estimating the daily PM2.5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01\u00b0 \u00d7 0.01\u00b0 spatial resolution","volume":"134","author":"Zhao","year":"2020","journal-title":"Environ. Int."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"119310","DOI":"10.1016\/j.atmosenv.2022.119310","article-title":"Estimating daily ground-level NO2 concentrations over China based on TROPOMI observations and machine learning approach","volume":"289","author":"Long","year":"2022","journal-title":"Atmos. Environ."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"119510","DOI":"10.1016\/j.envpol.2022.119510","article-title":"Spatiotemporal neural network for estimating surface NO2 concentrations over north China and their human health impact","volume":"307","author":"Zhang","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1021\/acs.est.9b03358","article-title":"Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging","volume":"54","author":"Di","year":"2020","journal-title":"Environ. Sci. Technol."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Chu, W., Zhang, C., Zhao, Y., Li, R., and Wu, P. (2022). Spatiotemporally Continuous Reconstruction of Retrieved PM2.5 Data Using an Autogeoi-Stacking Model in the Beijing-Tianjin-Hebei Region, China. Remote Sens., 14.","DOI":"10.3390\/rs14184432"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"117649","DOI":"10.1016\/j.atmosenv.2020.117649","article-title":"Advancing methodologies for applying machine learning and evaluating spatiotemporal models of fine particulate matter (PM2.5) using satellite data over large regions","volume":"239","author":"Just","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"104049","DOI":"10.1016\/j.scs.2022.104049","article-title":"PM2.5 concentration forecasting through a novel multi-scale ensemble learning approach considering intercity synergy","volume":"85","author":"Yu","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"106339","DOI":"10.1016\/j.atmosres.2022.106339","article-title":"Hybrid deep learning models for mapping surface NO2 across China: One complicated model, many simple models, or many complicated models?","volume":"278","author":"Liu","year":"2022","journal-title":"Atmos. Res."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1007\/s40747-021-00476-w","article-title":"An IoT enabled system for enhanced air quality monitoring and prediction on the edge","volume":"7","author":"Moursi","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.32604\/iasc.2022.023962","article-title":"Air Pollution Prediction Using Dual Graph Convolution LSTM Technique","volume":"33","author":"Ram","year":"2022","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"117711","DOI":"10.1016\/j.envpol.2021.117711","article-title":"Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia","volume":"288","author":"Kang","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"105823","DOI":"10.1016\/j.envint.2020.105823","article-title":"Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach","volume":"142","author":"Liu","year":"2020","journal-title":"Environ. Int."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"118285","DOI":"10.1016\/j.envpol.2021.118285","article-title":"Estimating 2013\u20132019 NO2 exposure with high spatiotemporal resolution in China using an ensemble model","volume":"292","author":"Huang","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"9475","DOI":"10.5194\/acp-21-9475-2021","article-title":"Separating emission and meteorological contributions to long-term PM2.5 trends over eastern China during 2000\u20132018","volume":"21","author":"Xiao","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Heidari, A.A., Akhoondzadeh, M., and Chen, H. (2022). A Wavelet PM2.5 Prediction System Using Optimized Kernel Extreme Learning with Boruta-XGBoost Feature Selection. Mathematics, 10.","DOI":"10.3390\/math10193566"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"118017","DOI":"10.1016\/j.eswa.2022.118017","article-title":"RCL-Learning: ResNet and convolutional long short-term memory-based spatiotemporal air pollutant concentration prediction model","volume":"207","author":"Zhang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.isatra.2019.11.023","article-title":"A feature selection and multi-model fusion-based approach of predicting air quality","volume":"100","author":"Zhang","year":"2020","journal-title":"ISA Trans."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1021\/acs.est.0c05815","article-title":"High-Resolution Spatiotemporal Modeling for Ambient PM2.5 Exposure Assessment in China from 2013 to 2019","volume":"55","author":"Huang","year":"2021","journal-title":"Environ. Sci. Technol."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Wang, D., Wang, H.W., Li, C., Lu, K.F., Peng, Z.R., Zhao, J., Fu, Q., and Pan, J. (2020). Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17249471"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"155324","DOI":"10.1016\/j.scitotenv.2022.155324","article-title":"An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment","volume":"834","author":"Faraji","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"119883","DOI":"10.1016\/j.envpol.2022.119883","article-title":"Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa","volume":"310","author":"Arowosegbe","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.apr.2019.11.019","article-title":"Prediction of outdoor PM2.5 concentrations based on a three-stage hybrid neural network model","volume":"11","author":"Liu","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.asr.2021.03.014","article-title":"Landslide susceptibility modelling based on AHC-OLID clustering algorithm","volume":"68","author":"Mao","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"133561","DOI":"10.1016\/j.scitotenv.2019.07.367","article-title":"Deep learning-based PM2.5 prediction considering the spatiotemporal correlations: A case study of Beijing, China","volume":"699","author":"Pak","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"39409","DOI":"10.1007\/s11356-021-12657-8","article-title":"A hybrid deep learning technology for PM2.5 air quality forecasting","volume":"28","author":"Zhang","year":"2021","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Li, S., Xie, G., Ren, J., Guo, L., Yang, Y., and Xu, X. (2020). Urban PM2.5 Concentration Prediction via Attention-Based CNN\u2013LSTM. Appl. Sci., 10.","DOI":"10.3390\/app10061953"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"11920","DOI":"10.1007\/s11356-021-16227-w","article-title":"Air quality prediction using CNN+LSTM-based hybrid deep learning architecture","volume":"29","author":"Gilik","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"120404","DOI":"10.1016\/j.envpol.2022.120404","article-title":"Air Quality Index prediction using an effective hybrid deep learning model","volume":"315","author":"Sarkar","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"101766","DOI":"10.1016\/j.apr.2023.101766","article-title":"Graph convolutional network\u2014Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation","volume":"14","author":"Ehteram","year":"2023","journal-title":"Atmos. Pollut. Res."},{"key":"ref_142","first-page":"1","article-title":"Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network","volume":"2020","author":"Guo","year":"2020","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Li, D., Liu, J., and Zhao, Y. (2022). Prediction of Multi-Site PM2.5 Concentrations in Beijing Using CNN-Bi LSTM with CBAM. Atmosphere, 13.","DOI":"10.3390\/atmos13101719"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s10098-021-02080-5","article-title":"Air pollution forecasting application based on deep learning model and optimization algorithm","volume":"24","author":"Heydari","year":"2022","journal-title":"Clean Technol. Environ. Policy"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"102237","DOI":"10.1016\/j.scs.2020.102237","article-title":"A Lag-FLSTM deep learning network based on Bayesian Optimization for multi-sequential-variant PM2.5 prediction","volume":"60","author":"Ma","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"106620","DOI":"10.1016\/j.asoc.2020.106620","article-title":"A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting","volume":"96","author":"Du","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_147","first-page":"1","article-title":"A Machine Learning Approach to Predict Air Quality in California","volume":"2020","author":"Castelli","year":"2020","journal-title":"Complexity"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"160446","DOI":"10.1016\/j.scitotenv.2022.160446","article-title":"Predicting hourly PM2.5 concentrations in wildfire-prone areas using a SpatioTemporal Transformer model","volume":"860","author":"Yu","year":"2023","journal-title":"Sci. Total. Environ."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s42834-023-00175-w","article-title":"Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model","volume":"33","author":"Cai","year":"2023","journal-title":"Sustain. Environ. Res."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"116459","DOI":"10.1016\/j.envpol.2021.116459","article-title":"A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2.5","volume":"273","author":"Yan","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"146288","DOI":"10.1016\/j.scitotenv.2021.146288","article-title":"Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017","volume":"778","author":"Guo","year":"2021","journal-title":"Sci. Total. Environ."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"105827","DOI":"10.1016\/j.envint.2020.105827","article-title":"Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States","volume":"142","author":"Ren","year":"2020","journal-title":"Environ. Int."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"116846","DOI":"10.1016\/j.envpol.2021.116846","article-title":"Using a land use regression model with machine learning to estimate ground level PM2.5","volume":"277","author":"Wong","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"135771","DOI":"10.1016\/j.scitotenv.2019.135771","article-title":"Air quality prediction at new stations using spatially transferred bi-directional long short-term memory network","volume":"705","author":"Ma","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"154279","DOI":"10.1016\/j.scitotenv.2022.154279","article-title":"Optimized neural network for daily-scale ozone prediction based on transfer learning","volume":"827","author":"Ma","year":"2022","journal-title":"Sci. Total. Environ."},{"key":"ref_156","first-page":"959","article-title":"Exploitation of Advanced Deep Learning Methods and Feature Modeling for Air Quality Prediction","volume":"36","author":"Parthiban","year":"2022","journal-title":"Rev. Dintelligence Artif."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"104869","DOI":"10.1016\/j.cageo.2021.104869","article-title":"Spatiotemporal causal convolutional network for forecasting hourly PM2.5 concentrations in Beijing, China","volume":"155","author":"Zhang","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.isprsjprs.2022.03.002","article-title":"Predicting annual PM2.5 in mainland China from 2014 to 2020 using multi temporal satellite product: An improved deep learning approach with spatial generalization ability","volume":"187","author":"Wang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"112573","DOI":"10.1016\/j.rse.2021.112573","article-title":"Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning","volume":"264","author":"Kim","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"3871","DOI":"10.1021\/acs.est.1c04076","article-title":"Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations","volume":"56","author":"Ren","year":"2022","journal-title":"Environ. Sci. Technol."},{"key":"ref_161","unstructured":"United States Environmental Protection Agency (2022). Learn About Heat Islands, US EPA."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"102542","DOI":"10.1016\/j.scs.2020.102542","article-title":"Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh","volume":"64","author":"Kafy","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"109910","DOI":"10.1016\/j.buildenv.2022.109910","article-title":"Measuring the relationship between morphological spatial pattern of green space and urban heat island using machine learning methods","volume":"228","author":"Lin","year":"2023","journal-title":"Build. Environ."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"109066","DOI":"10.1016\/j.buildenv.2022.109066","article-title":"Predicting the impacts of land use\/land cover changes on seasonal urban thermal characteristics using machine learning algorithms","volume":"217","author":"Kafy","year":"2022","journal-title":"Build. Environ."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhou, Y., Guo, X., and Qu, T. (2022). Calculation and Expression of the Urban Heat Island Indices Based on GeoSOT Grid. Sustainability, 14.","DOI":"10.3390\/su14052588"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Oh, J.W., Ngarambe, J., Duhirwe, P.N., Yun, G.Y., and Santamouris, M. (2020). Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-60632-z"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"101203","DOI":"10.1016\/j.uclim.2022.101203","article-title":"ArcUHI: A GIS add-in for automated modelling of the Urban Heat Island effect through machine learning","volume":"44","author":"Manchado","year":"2022","journal-title":"Urban Clim."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.isprsjprs.2021.04.009","article-title":"Statistical estimation of next-day nighttime surface urban heat islands","volume":"176","author":"Lai","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"101463","DOI":"10.1016\/j.uclim.2023.101463","article-title":"The future of Chinas urban heat island effects: A machine learning based scenario analysis on climatic-socioeconomic policies","volume":"49","author":"Lan","year":"2023","journal-title":"Urban Clim."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"503","DOI":"10.3390\/earth4030026","article-title":"Assessing Land Use\/Land Cover Changes and Urban Heat Island Intensification: A Case Study of Kamrup Metropolitan District, Northeast India (2000\u20132032)","volume":"4","author":"Choudhury","year":"2023","journal-title":"Earth"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"101729","DOI":"10.1016\/j.uclim.2023.101729","article-title":"Investigating and mapping day-night urban heat island and its driving factors using Sentinel\/MODIS data and Google Earth Engine. Case study: Greater Cairo, Egypt","volume":"52","author":"Abou","year":"2023","journal-title":"Urban Clim."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"103643","DOI":"10.1016\/j.scs.2021.103643","article-title":"Prediction and evaluation of spatial distributions of ozone and urban heat island using a machine learning modified land use regression method","volume":"78","author":"Han","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Garz\u00f3n, J., Molina, I., Velasco, J., and Calabia, A. (2021). A Remote Sensing Approach for Surface Urban Heat Island Modeling in a Tropical Colombian City Using Regression Analysis and Machine Learning Algorithms. Remote Sens., 13.","DOI":"10.3390\/rs13214256"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Waleed, M., Sajjad, M., Acheampong, A.O., and Alam, M.T. (2023). Towards Sustainable and Livable Cities: Leveraging Remote Sensing, Machine Learning, and Geo-Information Modelling to Explore and Predict Thermal Field Variance in Response to Urban Growth. Sustainability, 15.","DOI":"10.3390\/su15021416"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.patrec.2008.10.015","article-title":"Morphological segmentation of binary patterns","volume":"30","author":"Soille","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_176","unstructured":"NOAA National Severe Storms Laboratory (2022). Severe Weather 101: Flood Basics, NOAA National Severe Storms Laboratory."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"094052","DOI":"10.1088\/1748-9326\/aba5b3","article-title":"Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms","volume":"15","author":"Park","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"57345","DOI":"10.1007\/s11356-022-19903-7","article-title":"Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: Machine learning, risk prediction, and environmental impact","volume":"29","author":"Maged","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"4765","DOI":"10.1007\/s11269-022-03276-0","article-title":"New Machine Learning Ensemble for Flood Susceptibility Estimation","volume":"36","author":"Costache","year":"2022","journal-title":"Water Resour. Manag."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s00704-022-04068-7","article-title":"Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms","volume":"149","author":"Parvin","year":"2022","journal-title":"Theor. Appl. Climatol."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"3037","DOI":"10.1007\/s11269-020-02603-7","article-title":"Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping","volume":"34","author":"Yariyan","year":"2020","journal-title":"Water Resour. Manag."},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Costache, R., Pham, Q.B., Sharifi, E., Linh, N.T.T., Abba, S., Vojtek, M., Vojtekov\u00e1, J., Nhi, P.T.T., and Khoi, D.N. (2019). Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques. Remote Sens., 12.","DOI":"10.3390\/rs12010106"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"135161","DOI":"10.1016\/j.scitotenv.2019.135161","article-title":"Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method","volume":"711","author":"Hosseini","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J.J., Geertsema, M., Khosravi, K., Amini, A., and Bahrami, S. (2020). Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sens., 12.","DOI":"10.3390\/rs12020266"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"126382","DOI":"10.1016\/j.jhydrol.2021.126382","article-title":"XGBoost-based method for flash flood risk assessment","volume":"598","author":"Ma","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"135983","DOI":"10.1016\/j.scitotenv.2019.135983","article-title":"Integrated machine learning methods with resampling algorithms for flood susceptibility prediction","volume":"705","author":"Dodangeh","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"110485","DOI":"10.1016\/j.jenvman.2020.110485","article-title":"Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment","volume":"265","author":"Costache","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"2277","DOI":"10.1007\/s00477-020-01862-5","article-title":"Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms","volume":"34","author":"Talukdar","year":"2020","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"127877","DOI":"10.1016\/j.jhydrol.2022.127877","article-title":"Exploring the additional value of class imbalance distributions on interpretable flash flood susceptibility prediction in the Black Warrior River basin, Alabama, United States","volume":"610","author":"Koc","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"106081","DOI":"10.1016\/j.engappai.2023.106081","article-title":"The development of a road network flood risk detection model using optimised ensemble learning","volume":"122","author":"Wongthongtham","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_191","first-page":"215","article-title":"Flood susceptibility assessment using artificial neural networks in Indonesia","volume":"2","author":"Priscillia","year":"2021","journal-title":"Artif. Intell. Geosci."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"116813","DOI":"10.1016\/j.jenvman.2022.116813","article-title":"A novel framework for addressing uncertainties in machine learning-based geospatial approaches for flood prediction","volume":"326","author":"Adnan","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1007\/s12517-021-08610-1","article-title":"Spatial modeling of flood susceptibility using machine learning algorithms","volume":"14","author":"Meliho","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"136492","DOI":"10.1016\/j.scitotenv.2019.136492","article-title":"Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles","volume":"712","author":"Costache","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"2243884","DOI":"10.1080\/10106049.2023.2243884","article-title":"Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers","volume":"38","author":"Saleh","year":"2023","journal-title":"Geocarto Int."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"162285","DOI":"10.1016\/j.scitotenv.2023.162285","article-title":"Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery","volume":"873","author":"Seo","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"101625","DOI":"10.1016\/j.gsf.2023.101625","article-title":"Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model","volume":"14","author":"Pradhan","year":"2023","journal-title":"Geosci. Front."},{"key":"ref_198","doi-asserted-by":"crossref","unstructured":"Liu, J., Liu, K., and Wang, M. (2023). A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets. Remote Sens., 15.","DOI":"10.3390\/rs15092447"},{"key":"ref_199","doi-asserted-by":"crossref","unstructured":"Kaspi, M., and Kuleshov, Y. (2023). Flood Hazard Assessment in Australian Tropical Cyclone-Prone Regions. Climate, 11.","DOI":"10.3390\/cli11110229"},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"106379","DOI":"10.1016\/j.catena.2022.106379","article-title":"Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards","volume":"216","author":"Koc","year":"2022","journal-title":"CATENA"},{"key":"ref_201","unstructured":"United States Geological Survey (2022). What is a Landslide and What Causes One?, U.S. Geological Survey."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1007\/s11069-021-04731-8","article-title":"Landslide inventory and susceptibility models considering the landslide typology using deep learning: Himalayas, India","volume":"108","author":"Bera","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Chang, L., Zhang, R., and Wang, C. (2022). Evaluation and Prediction of Landslide Susceptibility in Yichang Section of Yangtze River Basin Based on Integrated Deep Learning Algorithm. Remote Sens., 14.","DOI":"10.3390\/rs14112717"},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Sun, X., Yu, C., Li, Y., and Rene, N.N. (2022). Susceptibility Mapping of Typical Geological Hazards in Helong City Affected by Volcanic Activity of Changbai Mountain, Northeastern China. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11060344"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"137320","DOI":"10.1016\/j.scitotenv.2020.137320","article-title":"Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning","volume":"720","author":"Dou","year":"2020","journal-title":"Sci. Total. Environ."},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"104451","DOI":"10.1016\/j.catena.2019.104451","article-title":"A spatially explicit deep learning neural network model for the prediction of landslide susceptibility","volume":"188","author":"Dao","year":"2020","journal-title":"CATENA"},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10346-019-01286-5","article-title":"Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan","volume":"17","author":"Dou","year":"2020","journal-title":"Landslides"},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"104580","DOI":"10.1016\/j.catena.2020.104580","article-title":"Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping","volume":"191","author":"Huang","year":"2020","journal-title":"CATENA"},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10346-019-01274-9","article-title":"A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.gr.2023.02.007","article-title":"Uncertainty analysis of non-landslide sample selection in landslide susceptibility prediction using slope unit-based machine learning models","volume":"117","author":"Chang","year":"2023","journal-title":"Gondwana Res."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"105364","DOI":"10.1016\/j.cageo.2023.105364","article-title":"Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling","volume":"176","author":"Dahal","year":"2023","journal-title":"Comput. Geosci."},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Liu, Z., and Xu, C. (2021). Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models. Front. Earth Sci., 9.","DOI":"10.3389\/feart.2021.589630"},{"key":"ref_213","doi-asserted-by":"crossref","unstructured":"Ye, C., Tang, R., Wei, R., Guo, Z., and Zhang, H. (2023). Generating accurate negative samples for landslide susceptibility mapping: A combined self-organizing-map and one-class SVM method. Front. Earth Sci., 10.","DOI":"10.3389\/feart.2022.1054027"},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1007\/s10064-022-02664-5","article-title":"Effectiveness of Newmark-based sampling strategy for coseismic landslide susceptibility mapping using deep learning, support vector machine, and logistic regression","volume":"81","author":"Xi","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/s10346-022-01998-1","article-title":"Handling data imbalance in machine learning based landslide susceptibility mapping: A case study of Mandakini River Basin, North-Western Himalayas","volume":"20","author":"Gupta","year":"2023","journal-title":"Landslides"},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"11581","DOI":"10.1109\/JSTARS.2021.3125741","article-title":"Landslide Susceptibility Prediction Based on Positive Unlabeled Learning Coupled With Adaptive Sampling","volume":"14","author":"Fang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1109\/LGRS.2020.2989497","article-title":"Landslide Susceptibility Modeling Using Bagging-Based Positive-Unlabeled Learning","volume":"18","author":"Wu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"104425","DOI":"10.1016\/j.catena.2019.104425","article-title":"A hybrid model considering spatial heterogeneity for landslide susceptibility mapping in Zhejiang Province, China","volume":"188","author":"Wang","year":"2020","journal-title":"CATENA"},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"2954","DOI":"10.1016\/j.jrmge.2023.03.001","article-title":"Uncertainties of landslide susceptibility prediction considering different landslide types","volume":"15","author":"Huang","year":"2023","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"ref_220","doi-asserted-by":"crossref","unstructured":"Sun, D., Chen, D., Zhang, J., Mi, C., Gu, Q., and Wen, H. (2023). Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation. Land, 12.","DOI":"10.3390\/land12051018"},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"110324","DOI":"10.1016\/j.asoc.2023.110324","article-title":"An explainable AI (XAI) model for landslide susceptibility modeling","volume":"142","author":"Pradhan","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"31175","DOI":"10.1109\/ACCESS.2022.3158328","article-title":"Predicting and Understanding Landslide Events With Explainable AI","volume":"10","author":"Collini","year":"2022","journal-title":"IEEE Access"},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"Fang, H., Shao, Y., Xie, C., Tian, B., Shen, C., Zhu, Y., Guo, Y., Yang, Y., Chen, G., and Zhang, M. (2023). A New Approach to Spatial Landslide Susceptibility Prediction in Karst Mining Areas Based on Explainable Artificial Intelligence. Sustainability, 15.","DOI":"10.3390\/su15043094"},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"117357","DOI":"10.1016\/j.jenvman.2023.117357","article-title":"Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model","volume":"332","author":"Zhang","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1109\/JSTARS.2020.3006192","article-title":"Unsupervised Feature Learning to Improve Transferability of Landslide Susceptibility Representations","volume":"13","author":"Zhu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"106799","DOI":"10.1016\/j.catena.2022.106799","article-title":"Landslide susceptibility assessment through TrAdaBoost transfer learning models using two landslide inventories","volume":"222","author":"Zhiyong","year":"2023","journal-title":"CATENA"},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"8765","DOI":"10.5194\/gmd-15-8765-2022","article-title":"Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning","volume":"15","author":"Wang","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.gsf.2020.05.010","article-title":"Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia","volume":"12","author":"Youssef","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"101619","DOI":"10.1016\/j.gsf.2023.101619","article-title":"An updating of landslide susceptibility prediction from the perspective of space and time","volume":"14","author":"Chang","year":"2023","journal-title":"Geosci. Front."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3374\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:53:46Z","timestamp":1760111626000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3374"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":229,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183374"],"URL":"https:\/\/doi.org\/10.3390\/rs16183374","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,11]]}}}