{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T11:29:29Z","timestamp":1776338969688,"version":"3.51.2"},"reference-count":104,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771538"],"award-info":[{"award-number":["41771538"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precipitation is the main factor that triggers landslides. Rainfall-induced landslide susceptibility mapping (LSM) is crucial for disaster prevention and disaster losses mitigation, though most studies are temporally ambiguous and on a regional scale. To better reveal landslide mechanisms and provide more accurate landslide susceptibility maps for landslide risk assessment and hazard prediction, developing a global dynamic LSM model is essential. In this study, we used Google Earth Engine (GEE) as the main data platform and applied three tree-based ensemble machine learning algorithms to construct global, dynamic rainfall-induced LSM models based on dynamic and static landslide influencing factors. The dynamic perspective is used in LSM: dynamic changes in landslide susceptibility can be identified on a daily scale. We note that Random Forest algorithm offers robust performance for accurate LSM (AUC = 0.975) and although the classification accuracy of LightGBM is the highest (AUC = 0.977), the results do not meet the sufficient conditions of a landslide susceptibility map. Combined with quantitative precipitation products, the proposed model can be used for the release of historical and predictive global dynamic landslide susceptibility information.<\/jats:p>","DOI":"10.3390\/rs14225795","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T03:27:44Z","timestamp":1668655664000},"page":"5795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Bohao","family":"Li","sequence":"first","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"},{"name":"Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, 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"}]},{"given":"Qian","family":"He","sequence":"additional","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave., Beijing 100875, China"}]},{"given":"Ziyu","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave., Beijing 100875, China"}]},{"given":"Weihua","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of National Safety and Emergency Management, Beijing Normal University, 19 Xinjiekou Wai Ave., Beijing 100875, China"}]},{"given":"Ningning","family":"Qiao","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":[[2022,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1007\/s12665-009-0394-9","article-title":"Comparison of Landslide Susceptibility Mapping Methodologies for Koyulhisar, Turkey: Conditional Probability, Logistic Regression, Artificial Neural Networks, and Support Vector Machine","volume":"61","author":"Yilmaz","year":"2010","journal-title":"Environ. Earth Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s11069-006-9104-z","article-title":"Use of Satellite Remote Sensing Data in the Mapping of Global Landslide Susceptibility","volume":"43","author":"Hong","year":"2007","journal-title":"Nat. Hazards"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.5194\/nhess-18-2161-2018","article-title":"Global Fatal Landslide Occurrence from 2004 to 2016","volume":"18","author":"Froude","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.catena.2018.12.035","article-title":"Exploring the Effects of the Design and Quantity of Absence Data on the Performance of Random Forest-Based Landslide Susceptibility Mapping","volume":"176","author":"Hong","year":"2019","journal-title":"Catena"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s12665-015-4795-7","article-title":"GIS-Based Landslide Susceptibility Mapping Using Analytical Hierarchy Process (AHP) and Certainty Factor (CF) Models for the Baozhong Region of Baoji City, China","volume":"75","author":"Chen","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.scitotenv.2019.01.221","article-title":"Assessment of Advanced Random Forest and Decision Tree Algorithms for Modeling Rainfall-Induced Landslide Susceptibility in the Izu-Oshima Volcanic Island, Japan","volume":"662","author":"Dou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1006","DOI":"10.1016\/j.scitotenv.2018.06.389","article-title":"Performance Evaluation of the GIS-Based Data Mining Techniques of Best-First Decision Tree, Random Forest, and Na\u00efve Bayes Tree for Landslide Susceptibility Modeling","volume":"644","author":"Chen","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cageo.2017.11.019","article-title":"Landslide Susceptibility Modeling Applying Machine Learning Methods: A Case Study from Longju in the Three Gorges Reservoir Area, China","volume":"112","author":"Zhou","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geomorph.2004.06.010","article-title":"The Application of GIS-Based Logistic Regression for Landslide Susceptibility Mapping in the Kakuda-Yahiko Mountains, Central Japan","volume":"65","author":"Ayalew","year":"2005","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1007\/s00254-006-0256-7","article-title":"Landslide Susceptibility Mapping in the Damrei Romel Area, Cambodia Using Frequency Ratio and Logistic Regression Models","volume":"50","author":"Lee","year":"2006","journal-title":"Environ. Geol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10346-012-0320-1","article-title":"Mapping Landslide Susceptibility with Logistic Regression, Multiple Adaptive Regression Splines, Classification and Regression Trees, and Maximum Entropy Methods: A Comparative Study","volume":"10","author":"Cuartero","year":"2013","journal-title":"Landslides"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1080\/10106049.2018.1510038","article-title":"Landslide Susceptibility Mapping Using Maximum Entropy and Support Vector Machine Models along the Highway Corridor, Garhwal Himalaya","volume":"35","author":"Pandey","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.catena.2017.01.010","article-title":"Landslide Susceptibility Assessment Using Maximum Entropy Model with Two Different Data Sampling Methods","volume":"152","author":"Kornejady","year":"2017","journal-title":"Catena"},{"key":"ref_15","first-page":"5693","article-title":"Artificial Neural Network and Sensitivity Analysis in the Landslide Susceptibility Mapping of Idukki District, India","volume":"37","author":"Saravanan","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/19475705.2018.1487471","article-title":"Mapping Earthquake-Triggered Landslide Susceptibility by Use of Artificial Neural Network (ANN) Models: An Example of the 2013 Minxian (China) Mw 5.9 Event","volume":"10","author":"Tian","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Lee, S., Hong, S.-M., and Jung, H.-S. (2017). A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea. Sustainability, 9.","DOI":"10.3390\/su9010048"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1007\/s12040-013-0282-2","article-title":"Landslide Susceptibility Mapping Using Support Vector Machine and GIS at the Golestan Province, Iran","volume":"122","author":"Pourghasemi","year":"2013","journal-title":"J. Earth Syst. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Quevedo, R.P., Maciel, D.A., Uehara, T.D.T., Vojtek, M., Renno, C.D., Pradhan, B., Vojtekova, J., and Pham, Q.B. (2021). Consideration of Spatial Heterogeneity in Landslide Susceptibility Mapping Using Geographical Random Forest Model. Geocarto Int., 1\u201324.","DOI":"10.1080\/10106049.2021.1996637"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Avand, M., Janizadeh, S., Naghibi, S.A., Pourghasemi, H.R., Khosrobeigi Bozchaloei, S., and Blaschke, T. (2019). A Comparative Assessment of Random Forest and K-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping. Water, 11.","DOI":"10.3390\/w11102076"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.enggeo.2010.09.009","article-title":"Landslide Susceptibility Mapping in Injae, Korea, Using a Decision Tree","volume":"116","author":"Yeon","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s11069-018-3256-5","article-title":"GIS-Based Evolutionary Optimized Gradient Boosted Decision Trees for Forest Fire Susceptibility Mapping","volume":"92","author":"Sachdeva","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide Susceptibility Mapping Using Random Forest, Boosted Regression Tree, Classification and Regression Tree, and General Linear Models and Comparison of Their Performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2016","journal-title":"Landslides"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"103225","DOI":"10.1016\/j.earscirev.2020.103225","article-title":"Machine Learning Methods for Landslide Susceptibility Studies: A Comparative Overview of Algorithm Performance","volume":"207","author":"Merghadi","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.catena.2018.03.003","article-title":"Review on Landslide Susceptibility Mapping Using Support Vector Machines","volume":"165","author":"Huang","year":"2018","journal-title":"Catena"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103812","DOI":"10.1016\/j.scs.2022.103812","article-title":"Predicting Future Urban Waterlogging-Prone Areas by Coupling the Maximum Entropy and FLUS Model","volume":"80","author":"Lin","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1007\/s10661-018-7052-1","article-title":"Assessing Fire Hazard Potential and Its Main Drivers in Mazandaran Province, Iran: A Data-Driven Approach","volume":"190","author":"Adab","year":"2018","journal-title":"Environ. Monit Assess"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.jenvman.2019.02.020","article-title":"Land Subsidence Hazard Modeling: Machine Learning to Identify Predictors and the Role of Human Activities","volume":"236","author":"Rahmati","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Juyal, A., and Sharma, S. (2021, January 4\u20136). A Study of Landslide Susceptibility Mapping Using Machine Learning Approach. Proceedings of the 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India.","DOI":"10.1109\/ICICV50876.2021.9388379"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geoderma.2017.06.020","article-title":"Landslide Spatial Modeling: Introducing New Ensembles of ANN, MaxEnt, and SVM Machine Learning Techniques","volume":"305","author":"Chen","year":"2017","journal-title":"Geoderma"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4271","DOI":"10.3390\/e17064271","article-title":"A Hybrid Physical and Maximum-Entropy Landslide Susceptibility Model","volume":"17","author":"Davis","year":"2015","journal-title":"Entropy"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, R., Yang, X., Xu, C., Wei, L., and Zeng, X. (2022). Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping. Remote Sens., 14.","DOI":"10.3390\/rs14020321"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"107084","DOI":"10.1016\/j.geomorph.2020.107084","article-title":"Regional Susceptibility Assessments with Heterogeneous Landslide Information: Slope Unit-vs. Pixel-Based Approach","volume":"356","author":"Jacobs","year":"2020","journal-title":"Geomorphology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105331","DOI":"10.1016\/j.enggeo.2019.105331","article-title":"Completeness Index for Earthquake-Induced Landslide Inventories","volume":"264","author":"Hakan","year":"2020","journal-title":"Eng. Geol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1007\/s10346-019-01136-4","article-title":"Rapid Prediction of the Magnitude Scale of Landslide Events Triggered by an Earthquake","volume":"16","author":"Persello","year":"2019","journal-title":"Landslides"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102973","DOI":"10.1016\/j.earscirev.2019.102973","article-title":"Geographical Landslide Early Warning Systems","volume":"200","author":"Guzzetti","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107804","DOI":"10.1016\/j.geomorph.2021.107804","article-title":"A Global Landslide Non-Susceptibility Map","volume":"389","author":"Jia","year":"2021","journal-title":"Geomorphology"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dilley, M. (2005). Natural Disaster Hotspots: A Global Risk Analysis, World Bank Publications.","DOI":"10.1596\/0-8213-5930-4"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"673","DOI":"10.5194\/nhess-9-673-2009","article-title":"Evaluation of a Preliminary Satellite-Based Landslide Hazard Algorithm Using Global Landslide Inventories","volume":"9","author":"Kirschbaum","year":"2009","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10346-006-0036-1","article-title":"Global Landslide and Avalanche Hotspots","volume":"3","author":"Nadim","year":"2006","journal-title":"Landslides"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1109\/TGRS.2006.888436","article-title":"An Experimental Global Prediction System for Rainfall-Triggered Landslides Using Satellite Remote Sensing and Geospatial Datasets","volume":"45","author":"Hong","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1002\/2017EF000715","article-title":"Satellite-based Assessment of Rainfall-triggered Landslide Hazard for Situational Awareness","volume":"6","author":"Kirschbaum","year":"2018","journal-title":"Earth\u2019s Future"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s11069-017-2757-y","article-title":"A Heuristic Approach to Global Landslide Susceptibility Mapping","volume":"87","author":"Stanley","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.5194\/nhess-17-1411-2017","article-title":"Landslide Susceptibility Mapping on a Global Scale Using the Method of Logistic Regression","volume":"17","author":"Lin","year":"2017","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/S0169-555X(01)00087-3","article-title":"Landslide Characteristics and Slope Instability Modeling Using GIS, Lantau Island, Hong Kong","volume":"42","author":"Dai","year":"2002","journal-title":"Geomorphology"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"105818","DOI":"10.1016\/j.enggeo.2020.105818","article-title":"Chrono-Validation of near-Real-Time Landslide Susceptibility Models via Plug-in Statistical Simulations","volume":"278","author":"Lombardo","year":"2020","journal-title":"Eng. Geol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.geomorph.2015.03.016","article-title":"Spatial and Temporal Analysis of a Global Landslide Catalog","volume":"249","author":"Kirschbaum","year":"2015","journal-title":"Geomorphology"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s11069-009-9401-4","article-title":"A Global Landslide Catalog for Hazard Applications: Method, Results, and Limitations","volume":"52","author":"Kirschbaum","year":"2010","journal-title":"Nat. Hazards"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Juang, C.S., Stanley, T.A., and Kirschbaum, D.B. (2019). Using Citizen Science to Expand the Global Map of Landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR). PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0218657"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3063","DOI":"10.5194\/nhess-22-3063-2022","article-title":"Estimating Global Landslide Susceptibility and Its Uncertainty through Ensemble Modeling","volume":"22","author":"Felsberg","year":"2022","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"101248","DOI":"10.1016\/j.gsf.2021.101248","article-title":"National-Scale Data-Driven Rainfall Induced Landslide Susceptibility Mapping for China by Accounting for Incomplete Landslide Data","volume":"12","author":"Lin","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.5194\/hess-14-1047-2010","article-title":"Probabilistic Modelling of Rainfall Induced Landslide Hazard Assessment","volume":"14","author":"Kawagoe","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Dikshit, A., Sarkar, R., Pradhan, B., Segoni, S., and Alamri, A.M. (2020). Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review. Appl. Sci., 10.","DOI":"10.3390\/app10072466"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10346-016-0711-9","article-title":"Spatial Prediction of Rainfall-Induced Landslides for the Lao Cai Area (Vietnam) Using a Hybrid Intelligent Approach of Least Squares Support Vector Machines Inference Model and Artificial Bee Colony Optimization","volume":"14","author":"Tuan","year":"2017","journal-title":"Landslides"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"7985","DOI":"10.1007\/s10668-019-00557-4","article-title":"Landslide Susceptibility and Influencing Factors Analysis in Rwanda","volume":"22","author":"Li","year":"2020","journal-title":"Environ. Dev. Sustain."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ado, M., Amitab, K., Maji, A.K., Jasi\u0144ska, E., Gono, R., Leonowicz, Z., and Jasi\u0144ski, M. (2022). Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey. Remote Sens., 14.","DOI":"10.3390\/rs14133029"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.geomorph.2015.07.012","article-title":"Quantitative Assessment of Landslide Susceptibility along the Xianshuihe Fault Zone, Tibetan Plateau, China","volume":"248","author":"Guo","year":"2015","journal-title":"Geomorphology"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Hartmann, J., and Moosdorf, N. (2012). The New Global Lithological Map Database GLiM: A Representation of Rock Properties at the Earth Surface. Geochem. Geophys. Geosyst., 13.","DOI":"10.1029\/2012GC004370"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.enggeo.2006.03.004","article-title":"A Comparative Study of Conventional, ANN Black Box, Fuzzy and Combined Neural and Fuzzy Weighting Procedures for Landslide Susceptibility Zonation in Darjeeling Himalayas","volume":"85","author":"Kanungo","year":"2006","journal-title":"Eng. Geol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1177\/8755293020944182","article-title":"The GEM Global Active Faults Database","volume":"36","author":"Styron","year":"2020","journal-title":"Earthq. Spectra"},{"key":"ref_63","unstructured":"Pagani, M., Garcia-Pelaez, J., Gee, R., Johnson, K., Poggi, V., Styron, R., Weatherill, G., Simionato, M., Vigan\u00f2, D., and Danciu, L. (2022, October 04). Global Earthquake Model (GEM) Seismic Hazard Map (Version 2018.1\u2013December 2018). Available online: https:\/\/www.globalquakemodel.org\/product\/global-hazard-map."},{"key":"ref_64","unstructured":"Das, D., and Agrawal, R. (2002). Physical Properties of Soils. Fundam. Soil Sci. New Delhi J. Indian Soc. Soil Sci., 283295."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1007\/s12517-010-0272-5","article-title":"Assessing Landslide Vulnerability from Soil Characteristics\u2014A GIS-Based Analysis","volume":"5","author":"Sharma","year":"2012","journal-title":"Arab. J. Geosci."},{"key":"ref_66","unstructured":"Jarvis, A., Reuter, H.I., Nelson, A., and Guevara, E. (2022, October 04). Hole-Filled SRTM for the Globe Version 4, Available from the CGIAR-CSI SRTM 90 m Database. Available online: http:\/\/srtm.csi.cgiar.org."},{"key":"ref_67","unstructured":"Earth Resources Observation and Science (EROS) Center (2022, October 04). Courtesy of the US Geological Survey, Available online: https:\/\/www.usgs.gov\/centers\/eros\/data-citation?qt-science_support_page_related_con=0."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.5194\/nhess-19-1973-2019","article-title":"GIS-Based Earthquake-Triggered-Landslide Susceptibility Mapping with an Integrated Weighted Index Model in Jiuzhaigou Region of Sichuan Province, China","volume":"19","author":"Yi","year":"2019","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s00254-007-0818-3","article-title":"GIS-Based Weights-of-Evidence Modelling of Rainfall-Induced Landslides in Small Catchments for Landslide Susceptibility Mapping","volume":"54","author":"Dahal","year":"2008","journal-title":"Environ. Geol"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1007\/s13762-013-0464-0","article-title":"GIS-Based Frequency Ratio and Index of Entropy Models for Landslide Susceptibility Assessment in the Caspian Forest, Northern Iran","volume":"11","author":"Jaafari","year":"2014","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1080\/10106049.2016.1195886","article-title":"Comparison of Landslide Susceptibility Mapping Based on Statistical Index, Certainty Factors, Weights of Evidence and Evidential Belief Function Models","volume":"32","author":"Cui","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Hong, Y., Adler, R., and Huffman, G. (2006). Evaluation of the Potential of NASA Multi-Satellite Precipitation Analysis in Global Landslide Hazard Assessment. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL028010"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"100364","DOI":"10.1016\/j.wace.2021.100364","article-title":"Investigating the Potential of a Global Precipitation Forecast to Inform Landslide Prediction","volume":"33","author":"Khan","year":"2021","journal-title":"Weather Clim. Extrem."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.geomorph.2014.03.033","article-title":"An Effective Antecedent Precipitation Model Derived from the Power-Law Relationship between Landslide Occurrence and Rainfall Level","volume":"216","author":"Ma","year":"2014","journal-title":"Geomorphology"},{"key":"ref_75","unstructured":"Mu\u00f1oz Sabater, J. (2019). ERA5-Land Hourly Data from 1981 to Present, The Copernicus Climate Change Service (C3S) Climate Data Store (CDS)."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging Predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach Learn"},{"key":"ref_77","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_78","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely Randomized Trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach Learn"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.elerap.2018.08.002","article-title":"Study on a Prediction of P2P Network Loan Default Based on the Machine Learning LightGBM and XGboost Algorithms According to Different High Dimensional Data Cleaning","volume":"31","author":"Ma","year":"2018","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_80","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017). Lightgbm: A Highly Efficient Gradient Boosting Decision Tree. Adv. Neural Inf. Process. Syst., 30, Available online: https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"3446","DOI":"10.1016\/j.eswa.2011.09.033","article-title":"An Experimental Comparison of Classification Algorithms for Imbalanced Credit Scoring Data Sets","volume":"39","author":"Brown","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from Class-Imbalanced Data: Review of Methods and Applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_83","unstructured":"Marceau, L., Qiu, L., Vandewiele, N., and Charton, E. (2019). A Comparison of Deep Learning Performances with Other Machine Learning Algorithms on Credit Scoring Unbalanced Data. arXiv Prepr."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"104364","DOI":"10.1016\/j.catena.2019.104364","article-title":"Investigating the Effects of Different Landslide Positioning Techniques, Landslide Partitioning Approaches, and Presence-Absence Balances on Landslide Susceptibility Mapping","volume":"187","author":"Pourghasemi","year":"2020","journal-title":"Catena"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"106103","DOI":"10.1016\/j.enggeo.2021.106103","article-title":"AI-Powered Landslide Susceptibility Assessment in Hong Kong","volume":"288","author":"Wang","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Khalid, S., Khalil, T., and Nasreen, S. (2014). A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning, IEEE.","DOI":"10.1109\/SAI.2014.6918213"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"137231","DOI":"10.1016\/j.scitotenv.2020.137231","article-title":"Modeling Landslide Susceptibility Using LogitBoost Alternating Decision Trees and Forest by Penalizing Attributes with the Bagging Ensemble","volume":"718","author":"Hong","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"107889","DOI":"10.1016\/j.geomorph.2021.107889","article-title":"Rapidly Assessing Earthquake-Induced Landslide Susceptibility on a Global Scale Using Random Forest","volume":"391","author":"He","year":"2021","journal-title":"Geomorphology"},{"key":"ref_89","unstructured":"Booth, G.D., Niccolucci, M.J., and Schuster, E.G. (1994). Identifying Proxy Sets in Multiple Linear Regression: An Aid to Better Coefficient Interpretation. Res. Pap. INT (USA), Available online: https:\/\/agris.fao.org\/agris-search\/search.do?recordID=US9439776."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A Review on Evaluation Metrics for Data Classification Evaluations","volume":"5","author":"Hossin","year":"2015","journal-title":"Int. J. Data Min. Knowl. Manag. Process"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1080\/10106049.2018.1559885","article-title":"A Novel Hybrid Approach of Landslide Susceptibility Modelling Using Rotation Forest Ensemble and Different Base Classifiers","volume":"35","author":"Pham","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-60191-3","article-title":"Assessing and Mapping Multi-Hazard Risk Susceptibility Using a Machine Learning Technique","volume":"10","author":"Pourghasemi","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Liu, S., Yin, K., Zhou, C., Gui, L., Liang, X., Lin, W., and Zhao, B. (2021). Susceptibility Assessment for Landslide Initiated along Power Transmission Lines. Remote Sens., 13.","DOI":"10.3390\/rs13245068"},{"key":"ref_94","first-page":"14","article-title":"Producing a Landslide Susceptibility Map through the Use of Analytic Hierarchical Process in Finikas Watershed, North Peloponnese, Greece","volume":"6","author":"Papadakis","year":"2017","journal-title":"Am. J. Geogr. Inf. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.catena.2018.04.003","article-title":"A Comparative Study of an Expert Knowledge-Based Model and Two Data-Driven Models for Landslide Susceptibility Mapping","volume":"166","author":"Zhu","year":"2018","journal-title":"Catena"},{"key":"ref_96","first-page":"346","article-title":"Spatio-Temporal Distribution of Slides (1999-2015) in Combeima\u2019s River Hydrographic Basin, Colombia","volume":"59","year":"2018","journal-title":"Rev. Geogr. Venez."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2195","DOI":"10.1007\/s10346-018-1008-y","article-title":"Landslide Inventory for Hazard Assessment in a Data-Poor Context: A Regional-Scale Approach in a Tropical African Environment","volume":"15","author":"Monsieurs","year":"2018","journal-title":"Landslides"},{"key":"ref_98","unstructured":"Pesevski, I., Jovanovski, M., and Nedelkovska, N. (2022, September 29). Republic of Macedonia Database, Available online: https:\/\/www.stat.gov.mk\/Default_en.aspx."},{"key":"ref_99","unstructured":"(2022, September 29). Mekong SERVIR-Mekong Myanmar Mapathon Landslides, Available online: https:\/\/gpm.nasa.gov\/landslides\/data.html."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1007\/s10346-017-0798-7","article-title":"Challenges for Landslide Hazard and Risk Management in \u2018Low-Risk\u2019Regions, Czech Republic\u2014Landslide Occurrences and Related Costs (IPL Project No. 197)","volume":"14","author":"Stemberk","year":"2017","journal-title":"Landslides"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"4323","DOI":"10.1007\/s10064-021-02238-x","article-title":"Capturing the Footprints of Ground Motion in the Spatial Distribution of Rainfall-Induced Landslides","volume":"80","author":"Kirschbaum","year":"2021","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_102","unstructured":"Benz., G., and Stanley, T. (2021, October 04). Pokot Landslide Inventory, Available online: https:\/\/gpm.nasa.gov\/landslides\/data.html."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Amatya, P., Kirschbaum, D., and Stanley, T. (2022). Rainfall-induced Landslide Inventories for Lower Mekong Based on Planet Imagery and a Semi-automatic Mapping Method. Geosci. Data J., Available online: https:\/\/rmets.onlinelibrary.wiley.com\/doi\/10.1002\/gdj3.145?af=R.","DOI":"10.1002\/gdj3.145"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Tavoularis, N., Papathanassiou, G., Ganas, A., and Argyrakis, P. (2021). Development of the Landslide Susceptibility Map of Attica Region, Greece, Based on the Method of Rock Engineering System. 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