{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:09:32Z","timestamp":1774613372162,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T00:00:00Z","timestamp":1675123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan","award":["2017YFC1502902"],"award-info":[{"award-number":["2017YFC1502902"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide is a natural disaster that seriously affects human life and social development. In this study, the characteristics and effectiveness of convolutional neural network (CNN) and conventional machine learning (ML) methods in a landslide susceptibility assessment (LSA) are compared. Six ML methods used in this study are Adaboost, multilayer perceptron neural network (MLP-NN), random forest (RF), naive Bayes, decision tree (DT), and gradient boosting decision tree (GBDT). First, the basic knowledge and structures of the CNN and ML methods, and the steps of the LSA are introduced. Then, 11 conditioning factors in three categories in the Hongxi River Basin, Pingwu County, Mianyang City, Sichuan Province are chosen to build the train, validation, and test samples. The CNN and ML models are constructed based on these samples. For comparison, indicator methods, statistical methods, and landslide susceptibility maps (LSMs) are used. The result shows that the CNN can obtain the highest accuracy (86.41%) and the highest AUC (0.9249) in the LSA. The statistical methods represented by the mean and variance of TP and TN perform more firmly on the possibility of landslide occurrence. Furthermore, the LSMs show that all models can successfully identify most of the landslide points, but for areas with a low frequency of landslides, some models are insufficient. The CNN model demonstrates better results in the recognition of the landslides\u2019 cluster region, this is also related to the convolution operation that takes the surrounding environment information into account. The higher accuracy and more concentrative possibility of CNN in LSA is of great significance for disaster prevention and mitigation, which can help the efficient use of human and material resources. Although CNN performs better than other methods, there are still some limitations, the identification of low-cluster landside areas can be enhanced by improving the CNN model.<\/jats:p>","DOI":"10.3390\/rs15030798","type":"journal-article","created":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T05:33:53Z","timestamp":1675229633000},"page":"798","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Comparisons of Convolutional Neural Network and Other Machine Learning Methods in Landslide Susceptibility Assessment: A Case Study in Pingwu"],"prefix":"10.3390","volume":"15","author":[{"given":"Ziyu","family":"Jiang","sequence":"first","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave., Beijing 100875, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, 19 Xinjiekou Wai Ave., Beijing 100875, China"}]},{"given":"Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave., Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7687-7824","authenticated-orcid":false,"given":"Kai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave., Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s10346-015-0674-2","article-title":"Sediment Failure Types, Preconditions and Triggering Factors in the Gulf of Cadiz","volume":"14","author":"Leynaud","year":"2017","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.enggeo.2017.10.006","article-title":"Characteristics of Rainfall Intensity, Duration, and Kinetic Energy for Landslide Triggering in Taiwan","volume":"231","author":"Chang","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1007\/s11069-021-04601-3","article-title":"Torrential Rainfall-Induced Landslide Susceptibility Assessment Using Machine Learning and Statistical Methods of Eastern Himalaya","volume":"107","author":"Chowdhuri","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1007\/s10064-019-01716-7","article-title":"Risk Assessment of Seismic Landslides Based on Analysis of Historical Earthquake Disaster Characteristics","volume":"79","author":"Tang","year":"2020","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1007\/s10346-015-0643-9","article-title":"Landslide Impacts in Germany: A Historical and Socioeconomic Perspective","volume":"13","author":"Klose","year":"2016","journal-title":"Landslides"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4325","DOI":"10.1007\/s10064-018-1415-2","article-title":"Automatic Calculation of Rainfall Thresholds for Landslide Occurrence in Chukha Dzongkhag, Bhutan","volume":"78","author":"Gariano","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_7","first-page":"1623","article-title":"Geodetic Studies with Significant Contribution to Landslide Monitoring in South-Western Romania\u2014Area with High Risk Potential","volume":"23","author":"Vilceanu","year":"2016","journal-title":"Teh. Vjesn."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10346-011-0276-6","article-title":"The Characteristics and Failure Mechanism of the Largest Landslide Triggered by the Wenchuan Earthquake, May 12, 2008, China","volume":"9","author":"Huang","year":"2012","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1007\/s10346-021-01622-8","article-title":"Regional-Scale Landslide Risk Assessment on Mt. Umyeon Using Risk Index Estimation","volume":"18","author":"Nguyen","year":"2021","journal-title":"Landslides"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Mohammadi, A., Shahabi, H., Bin Ahmad, B., Al-Ansari, N., Shirzadi, A., Clague, J.J., Jaafari, A., Chen, W., and Nguyen, H. (2020). Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17144933"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"676","DOI":"10.20965\/jdr.2020.p0676","article-title":"Generation of Risk Information Based on Comprehensive Real-Time Analysis of Flooding and Landslide Disaster Occurrence Hazard and Social Vulnerability","volume":"12","author":"Sano","year":"2020","journal-title":"J. Disaster Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106621","DOI":"10.1016\/j.ecolind.2020.106621","article-title":"Assessment of Landscape Ecological Risk for a Cross-Border Basin: A Case Study of the Koshi River Basin, Central Himalayas","volume":"117","author":"Wang","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s12665-018-7524-1","article-title":"Assessing LNRF, FR, and AHP Models in Landslide Susceptibility Mapping Index: A Comparative Study of Nojian Watershed in Lorestan Province, Iran","volume":"77","author":"Abedini","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s12205-018-0156-x","article-title":"Landslide Susceptibility Mapping Using Relative Frequency and Predictor Rate along Araniko Highway","volume":"23","author":"Acharya","year":"2019","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5457","DOI":"10.1007\/s10706-019-00992-0","article-title":"Landslide Susceptibility Assessment Using Evidence Belief Function and Frequency Ratio Models in Taounate City (North of Morocco)","volume":"37","author":"Abidi","year":"2019","journal-title":"Geotech. Geol. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s12665-016-5400-4","article-title":"Application of Statistical Index and Index of Entropy Methods to Landslide Susceptibility Assessment in Gongliu (Xinjiang, China)","volume":"75","author":"Wang","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-Based Learning Applied to Document Recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_19","unstructured":"Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., and Garnett, R. (2015, January 7\u201312). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1166\/jmihi.2021.3283","article-title":"Image Visual Sensor Used in Health-Care Navigation in Indoor Scenes Using Deep Reinforcement Learning (DRL) and Control Sensor Robot for Patients Data Health Information","volume":"11","author":"Seaman","year":"2021","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"044503","DOI":"10.1088\/1538-3873\/ab7548","article-title":"Celestial Spectra Classification Network Based on Residual and Attention Mechanisms","volume":"132","author":"Zou","year":"2020","journal-title":"Publ. Astron. Soc. Pac."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1007\/s13753-019-00233-1","article-title":"Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China","volume":"10","author":"Zhang","year":"2019","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","article-title":"Long Short-Term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data","volume":"54","author":"Ma","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","article-title":"A Survey of Deep Neural Network Architectures and Their Applications","volume":"234","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2019EA000812","DOI":"10.1029\/2019EA000812","article-title":"A Deep Learning-Based Methodology for Precipitation Nowcasting with Radar","volume":"7","author":"Chen","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Saha, S., Saha, A., Hembram, T.K., Pradhan, B., and Alamri, A.M. (2020). Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya. Appl. Sci., 10.","DOI":"10.3390\/app10113772"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5235","DOI":"10.1109\/JSTARS.2021.3079196","article-title":"Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions","volume":"14","author":"Cai","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.procs.2012.09.069","article-title":"Adaptive Machine Learning Approaches to Seasonal Prediction of Tropical Cyclones","volume":"12","author":"Richman","year":"2012","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Arabameri, A., Saha, S., Roy, J., Chen, W., Blaschke, T., and Tien Bui, D. (2020). Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran. Remote Sens., 12.","DOI":"10.3390\/rs12030475"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1016\/j.scitotenv.2018.01.124","article-title":"Landslide Susceptibility Modelling Using GIS-Based Machine Learning Techniques for Chongren County, Jiangxi Province, China","volume":"626","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.catena.2018.01.005","article-title":"Landslide Susceptibility Mapping Using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest Ensembles in the Guangchang Area (China)","volume":"163","author":"Hong","year":"2018","journal-title":"Catena"},{"key":"ref_33","first-page":"47","article-title":"Identification of Landslides and Debris Flows Using Semi-Variance Model: A Case Study of Hongxi Basin in Sichuan","volume":"35","author":"Li","year":"2019","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Di Napoli, M., Marsiglia, P., Di Martire, D., Ramondini, M., Ullo, S.L., and Calcaterra, D. (2020). Landslide Susceptibility Assessment of Wildfire Burnt Areas through Earth-Observation Techniques and a Machine Learning-Based Approach. Remote Sens., 12.","DOI":"10.3390\/rs12152505"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kadavi, P.R., Lee, C.-W., and Lee, S. (2018). Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping. Remote Sens., 10.","DOI":"10.3390\/rs10081252"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.geomorph.2016.02.012","article-title":"Landslide Susceptibility Assessment in Lianhua County (China): A Comparison between a Random Forest Data Mining Technique and Bivariate and Multivariate Statistical Models","volume":"259","author":"Hong","year":"2016","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1080\/19475705.2018.1451399","article-title":"Application and Comparison of Logistic Regression Model and Neural Network Model in Earthquake-Induced Landslides Susceptibility Mapping at Mountainous Region, China","volume":"9","author":"Xie","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107201","DOI":"10.1016\/j.geomorph.2020.107201","article-title":"A Random Forest Model of Landslide Susceptibility Mapping Based on Hyperparameter Optimization Using Bayes Algorithm","volume":"362","author":"Sun","year":"2020","journal-title":"Geomorphology"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104833","DOI":"10.1016\/j.catena.2020.104833","article-title":"GIS-Based Landslide Susceptibility Assessment Using Optimized Hybrid Machine Learning Methods","volume":"196","author":"Chen","year":"2021","journal-title":"Catena"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s10346-015-0557-6","article-title":"Spatial Prediction Models for Shallow Landslide Hazards: A Comparative Assessment of the Efficacy of Support Vector Machines, Artificial Neural Networks, Kernel Logistic Regression, and Logistic Model Tree","volume":"13","author":"Tuan","year":"2016","journal-title":"Landslides"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Menze, B.H., Kelm, B.M., Masuch, R., Himmelreich, U., Bachert, P., Petrich, W., and Hamprecht, F.A. (2009). A Comparison of Random Forest and Its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data. BMC Bioinform., 16.","DOI":"10.1186\/1471-2105-10-213"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_43","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. Beyond Classification: Structured Regression for Robust Cell Detection Using Convolutional Neural Network. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s00521-016-2700-2","article-title":"Design of Memristor-Based Image Convolution Calculation in Convolutional Neural Network","volume":"30","author":"Zeng","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1111\/j.1467-8306.2004.09402003.x","article-title":"Tobler\u2019s First Law of Geography: A Big Idea for a Small World?","volume":"94","author":"Sui","year":"2004","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 7\u201313). Learning Spatiotemporal Features with 3D Convolutional Networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully Convolutional Networks for Semantic Segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, L., Choy, C.-S., and Li, Y.-W. (2016, January 13\u201316). Deep Sparse Rectifier Neural Networks for Speech Denoising. Proceedings of the 2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC), Xi\u2019an, China.","DOI":"10.1109\/IWAENC.2016.7602891"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s10822-016-9895-2","article-title":"Improving Quantitative Structure\u2013Activity Relationship Models Using Artificial Neural Networks Trained with Dropout","volume":"30","author":"Mendenhall","year":"2016","journal-title":"J. Comput. Aided Mol. Des."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1023\/A:1007649029923","article-title":"BoosTexter: A Boosting-Based System for Text Categorization","volume":"39","author":"Schapire","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"104396","DOI":"10.1016\/j.catena.2019.104396","article-title":"Application of Alternating Decision Tree with AdaBoost and Bagging Ensembles for Landslide Susceptibility Mapping","volume":"187","author":"Wu","year":"2020","journal-title":"Catena"},{"key":"ref_52","unstructured":"Delshadpour, S. (2003, January 20\u201322). Improved MLP Neural Network as Chromosome Classifier. Proceedings of the IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, Osaka-Nara, Japan."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"140549","DOI":"10.1016\/j.scitotenv.2020.140549","article-title":"Introducing a Novel Multi-Layer Perceptron Network Based on Stochastic Gradient Descent Optimized by a Meta-Heuristic Algorithm for Landslide Susceptibility Mapping","volume":"742","author":"Hong","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_54","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_55","unstructured":"Rish, I. (2001, January 4\u20136). An Empirical Study of the Na\u00efve Bayes Classifier. Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, Seattle, WA, USA."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Nhu, V.-H., Shirzadi, A., Shahabi, H., Singh, S.K., Al-Ansari, N., Clague, J.J., Jaafari, A., Chen, W., Miraki, S., and Dou, J. (2020). Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naive Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17082749"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1613\/jair.279","article-title":"Improved Use of Continuous Attributes in C4.5","volume":"4","author":"Quinlan","year":"1996","journal-title":"J. Artif. Intell. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"134979","DOI":"10.1016\/j.scitotenv.2019.134979","article-title":"Modeling FLood Susceptibility Using Data-Driven Approaches of Na\u00efve Bayes Tree, Alternating Decision Tree, and Random Forest Methods","volume":"11","author":"Chen","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_59","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_60","doi-asserted-by":"crossref","unstructured":"Song, Y., Niu, R., Xu, S., Ye, R., Peng, L., Guo, T., Li, S., and Chen, T. (2019). Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China). ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010004"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s40677-020-00155-x","article-title":"GIS-Based Landslide Susceptibility Mapping and Assessment Using Bivariate Statistical Methods in Simada Area, Northwestern Ethiopia","volume":"7","author":"Mersha","year":"2020","journal-title":"Geoenviron. Disasters"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Merghadi, A., Abderrahmane, B., and Tien Bui, D. (2018). Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070268"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/798\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:19:26Z","timestamp":1760120366000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/798"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,31]]},"references-count":62,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030798"],"URL":"https:\/\/doi.org\/10.3390\/rs15030798","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,31]]}}}