{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:07:20Z","timestamp":1777500440820,"version":"3.51.4"},"reference-count":97,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T00:00:00Z","timestamp":1601337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport","award":["19TSRDB151228-01"],"award-info":[{"award-number":["19TSRDB151228-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Landslides impact on human activities and socio-economic development, especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for the rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides, i.e., topographic, hydrologic, soil, forest, and geologic factors, are prepared from various sources based on availability, and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performed field surveys. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories contain 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models, i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN), are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.756 and the testing accuracy is 0.703. Similarly, the training accuracy of XGBoost is 0.757 and testing accuracy is 0.74. The prediction of DNN revealed acceptable agreement between the susceptibility map and the existing landslides, with a training accuracy of 0.855 and testing accuracy of 0.802. The results showed that the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area.<\/jats:p>","DOI":"10.3390\/ijgi9100569","type":"journal-article","created":{"date-parts":[[2020,9,29]],"date-time":"2020-09-29T20:56:22Z","timestamp":1601412982000},"page":"569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3281-7739","authenticated-orcid":false,"given":"Ananta Man Singh","family":"Pradhan","sequence":"first","affiliation":[{"name":"Water Resources Research and Development Center, Ministry of Energy, Water Resources and Irrigation, Government of Nepal, Pulchok, Lalitpur 44700, Nepal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun-Tae","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Ocean Engineering, Geo-Systems Engineering Laboratory, Pukyong National University, 45 Yongso-ro, Nam-gu, Busan 48513, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1023\/B:CLIM.0000043141.54763.f8","article-title":"On Climate Variations and Changes Observed in South Korea","volume":"66","author":"Chung","year":"2004","journal-title":"Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kim, Y.-T., and Lee, J.-S. (2013). Slope Stability Characteristic of Unsaturated Weathered Granite Soil in Korea considering Antecedent Rainfall. Geo Congr. 2013, 349\u2013401.","DOI":"10.1061\/9780784412787.039"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.2113\/gseegeosci.6.1.25","article-title":"Evaluation of seismic slope-performance models using a regional case study","volume":"6","author":"Miles","year":"2000","journal-title":"Environ. Eng. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"663","DOI":"10.5194\/hess-10-663-2006","article-title":"A new method for determination of most likely landslide initiation points and the evaluation of digital terrain model scale in terrain stability mapping","volume":"10","author":"Tarolli","year":"2006","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_5","first-page":"1","article-title":"A hydrological method of estimation of the topographic effect on the saturated throughflow","volume":"5","author":"Iida","year":"1984","journal-title":"Jpn. Geomorph. Union Trans."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1023\/A:1021274710840","article-title":"Investigating landslides caused by earthquakes\u2014A historical review","volume":"23","author":"Keefer","year":"2002","journal-title":"Surv. Geophys."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"17465","DOI":"10.1029\/JB094iB12p17465","article-title":"Prodigious submarine landslides on the Hawaiian Ridge","volume":"94","author":"Moore","year":"1989","journal-title":"J. Geophys. Res."},{"key":"ref_8","unstructured":"Brabb, E.E. (1984, January 23\u201331). Innovative Approaches to Landslide Hazard Mapping. Proceedings of the 4th International Symposium on Landslides, Toronto, ON, Canada."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.earscirev.2014.12.005","article-title":"Is the present the key to the future?","volume":"142","author":"Furlani","year":"2015","journal-title":"Earth-Sci. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1002\/esp.3290160505","article-title":"GIS techniques and statistical models in evaluating landslide hazard","volume":"16","author":"Carrara","year":"1991","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1023\/B:NHAZ.0000007172.62651.2b","article-title":"Validation of Spatial Prediction Models for Landslide Hazard Mapping","volume":"30","author":"Chung","year":"2003","journal-title":"Nat. Hazards"},{"key":"ref_12","first-page":"209","article-title":"Recommendations for the quantitative analysis of landslide risk","volume":"73","author":"Corominas","year":"2013","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth-Sci. Rev."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1007\/s12040-018-0936-1","article-title":"Assessment of co-seismic landslide susceptibility using LR and ANCOVA in Barpak region, Nepal","volume":"127","author":"Shrestha","year":"2018","journal-title":"J. Earth Syst. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s100640050066","article-title":"Landslide hazard assessment: Summary review and new perspectives","volume":"58","author":"Aleotti","year":"1999","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1144\/1470-9236\/02-039","article-title":"Quantitative Landslide Hazard and Risk Assessment: A Case Study","volume":"36","author":"Flentje","year":"2003","journal-title":"Q. J. Eng. Geol. Hydrogeol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1007\/s11069-014-1065-z","article-title":"Relative effect method of landslide susceptibility zonation in weathered granite soil: A case study in Deokjeok-ri Creek, South Korea","volume":"72","author":"Pradhan","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3126\/jngs.v44i0.24483","article-title":"Use of different bivariate statistical landslide susceptibility methods: A case study of Khulekhani watershed, Nepal","volume":"44","author":"Pradhan","year":"2012","journal-title":"J. Nepal Geol. Soc."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.catena.2018.01.005","article-title":"Bin 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_21","doi-asserted-by":"crossref","unstructured":"Pourghasemi, H., Gayen, A., Park, S., Lee, C.-W., Lee, S., Pourghasemi, H.R., Gayen, A., Park, S., Lee, C.-W., and Lee, S. (2018). Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and Na\u00efveBayes Machine-Learning Algorithms. Sustainability, 10.","DOI":"10.3390\/su10103697"},{"key":"ref_22","first-page":"1","article-title":"A Novel Hybrid Approach of Landslide Susceptibility Modeling Using Rotation Forest Ensemble and Different Base Classifiers","volume":"14","author":"Prakash","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.catena.2018.10.004","article-title":"A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil","volume":"173","author":"Pham","year":"2019","journal-title":"CATENA"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kornejady, A., Pourghasemi, H.R., and Afzali, S.F. (2019). Presentation of RFFR New Ensemble Model for Landslide Susceptibility Assessment in Iran, Springer.","DOI":"10.1007\/978-3-319-77377-3_7"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104458","DOI":"10.1016\/j.catena.2020.104458","article-title":"Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area","volume":"188","author":"Nhu","year":"2020","journal-title":"Catena"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.ecolmodel.2009.11.008","article-title":"New approaches to modelling fish\u2013habitat relationships","volume":"221","author":"Knudby","year":"2010","journal-title":"Ecol. Modell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1080\/10106049.2016.1188166","article-title":"Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods","volume":"32","author":"Falah","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1016\/j.scitotenv.2019.06.205","article-title":"Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility","volume":"688","author":"Arabameri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.jhydrol.2018.12.002","article-title":"Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques","volume":"569","author":"Darabi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kavzoglu, T., Colkesen, I., and Sahin, E.K. (2019). Machine Learning Techniques in Landslide Susceptibility Mapping: A Survey and a Case Study, Springer.","DOI":"10.1007\/978-3-319-77377-3_13"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nguyen, V., Pham, B., Vu, B., Prakash, I., Jha, S., Shahabi, H., Shirzadi, A., Ba, D., Kumar, R., and Chatterjee, J. (2019). Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling. Forests, 10.","DOI":"10.3390\/f10020157"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104358","DOI":"10.1016\/j.catena.2019.104358","article-title":"Systematic sample subdividing strategy for training landslide susceptibility models","volume":"187","author":"Sameen","year":"2020","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/BF02635432","article-title":"Landslides inventory","volume":"12","year":"1975","journal-title":"Bull. Int. Assoc. Eng. Geol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12594-020-1385-4","article-title":"Generating Substantially Complete Landslide Inventory Using Multiple Data Sources: A Case Study in Northwest Himalayas, India","volume":"95","author":"Ghosh","year":"2020","journal-title":"J. Geol. Soc. India"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10346-007-0112-1","article-title":"The rainfall intensity\u2013duration control of shallow landslides and debris flows: An update","volume":"5","author":"Guzzetti","year":"2008","journal-title":"Landslides"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105572","DOI":"10.1016\/j.enggeo.2020.105572","article-title":"Landslide susceptibility assessment based on an incomplete landslide inventory in the Jilong Valley, Tibet, Chinese Himalayas","volume":"270","author":"Du","year":"2020","journal-title":"Eng. Geol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"907","DOI":"10.3390\/rs6020907","article-title":"Integration of Remote Sensing Techniques for Intensity Zonation within a Landslide Area: A Case Study in the Northern Apennines, Italy","volume":"6","author":"Tofani","year":"2014","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/17445647.2019.1651770","article-title":"PS-driven inventory of town-damaging landslides in the Benevento, Avellino and Salerno Provinces, southern Italy","volume":"15","author":"Guerriero","year":"2019","journal-title":"J. Maps"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10346-017-0861-4","article-title":"The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: Geomorphological features and landslide distribution","volume":"15","author":"Rosi","year":"2018","journal-title":"Landslides"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1111\/j.0906-7590.2008.5203.x","article-title":"Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation","volume":"31","author":"Phillips","year":"2008","journal-title":"Ecography"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.catena.2016.01.022","article-title":"Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping","volume":"140","author":"Pradhan","year":"2016","journal-title":"Catena"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10346-018-1112-z","article-title":"A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea","volume":"16","author":"Pradhan","year":"2019","journal-title":"Landslides"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1080\/10106049.2016.1140824","article-title":"A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping","volume":"32","author":"Chen","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1007\/s000240050017","article-title":"Applying Probability Determination to Refine Landslide-triggering Rainfall Thresholds Using an Empirical \u201cAntecedent Daily Rainfall Model\u201d","volume":"157","author":"Glade","year":"2000","journal-title":"Pure Appl. Geophys."},{"key":"ref_45","unstructured":"Crozier, M.J., and Glade, T. (2012). A Review of Scale Dependency in Landslide Hazard and Risk Analysis. Landslide Hazard and Risk, Wiley Online Library."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.enggeo.2004.06.001","article-title":"Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)","volume":"75","author":"Ercanoglu","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s002540000163","article-title":"Assessment of landslide susceptibility on the natural terrain of Lantau Island, Hong Kong","volume":"40","author":"Dai","year":"2001","journal-title":"Environ. Geol."},{"key":"ref_48","unstructured":"Doornkamp, J.C., and Cooke, R.U. (1974). Geomorphology in Environmental Management: An Introduction, Clarendon Press."},{"key":"ref_49","first-page":"17502","article-title":"Effect of spatial soil depth distribution model on shallow landslide prediction: A case study from Korean Mountain","volume":"20","author":"Pradhan","year":"2018","journal-title":"EGUA"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s12665-011-1297-0","article-title":"Landslide susceptibility assessment: What are the effects of mapping unit and mapping method?","volume":"66","author":"Erener","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/0013-7952(92)90020-Y","article-title":"Landslide hazard mapping based on geological attributes","volume":"32","author":"Pachauri","year":"1992","journal-title":"Eng. Geol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital terrain modelling: A review of hydrological, geomorphological, and biological applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.2136\/sssaj1986.03615995005000050042x","article-title":"Physical Basis of the Length-slope Factor in the Universal Soil Loss Equation1","volume":"50","author":"Moore","year":"1986","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scitotenv.2019.01.329","article-title":"Landslide spatial modelling using novel bivariate statistical based Na\u00efve Bayes, RBF Classifier, and RBF Network machine learning algorithms","volume":"663","author":"He","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/02626667909491834","article-title":"A physically based, variable contributing area model of basin hydrology","volume":"24","author":"Beven","year":"1979","journal-title":"Hydrol. Sci. Bull."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"437","DOI":"10.3188\/szf.2002.0437","article-title":"Wirkungen des Waldes auf oberfl\u00e4chennahe Rutschprozesse|Effects of forest on landslides","volume":"153","author":"Rickli","year":"2002","journal-title":"Schweiz. Z. Forstwes."},{"key":"ref_57","first-page":"611","article-title":"Influence of soil properties on landslide occurrences in Bududa district, Eastern Uganda","volume":"4","author":"Kitutu","year":"2009","journal-title":"Afr. J. Agric. Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sidle, R.C., Pearce, A.J., O\u2019Loughlin, C.L., and American Geophysical Union (1985). Hillslope Stability and Land Use, American Geophysical Union.","DOI":"10.1029\/WM011"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.clay.2007.01.007","article-title":"The effects of clay on landslides: A case study","volume":"38","author":"Yalcin","year":"2007","journal-title":"Appl. Clay Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1002\/esp.4281","article-title":"Lithological controls on hillslope sediment supply: Insights from landslide activity and grain size distributions","volume":"43","author":"Duna","year":"2018","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1007\/s10346-013-0391-7","article-title":"Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression","volume":"11","author":"Kavzoglu","year":"2014","journal-title":"Landslides"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10064-017-1055-y","article-title":"An ensemble landslide hazard model incorporating rainfall threshold for Mt. Umyeon, South Korea","volume":"78","author":"Pradhan","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","article-title":"A caution regarding rules of thumb for variance inflation factors","volume":"41","year":"2007","journal-title":"Qual. Quant."},{"key":"ref_64","unstructured":"Menard, S. (1995). Applied Logistic Regression Analysis, SAGE."},{"key":"ref_65","first-page":"R1","article-title":"Multiple regression for physiological data analysis: The problem of multicollinearity","volume":"249","author":"Slinker","year":"1985","journal-title":"Am. J. Physiol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1732","DOI":"10.1161\/CIRCULATIONAHA.106.654376","article-title":"Multiple linear regression: Accounting for multiple simultaneous determinants of a continuous dependent variable","volume":"117","author":"Slinker","year":"2008","journal-title":"Circulation"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Belsley, D., Kuh, E., and Welsch, R. (1980). Detecting and Assessing Collinearity. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, Wiley.","DOI":"10.1002\/0471725153"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1126\/science.462188","article-title":"Assessment of diagnostic technologies","volume":"205","author":"Swets","year":"1979","journal-title":"Science"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., and Lemeshow, S. (2000). Applied Logistic Regression, Wiley-Blackwell. [2nd ed.].","DOI":"10.1002\/0471722146"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1093\/bioinformatics\/16.5.412","article-title":"Assessing the accuracy of prediction algorithms for classification: An overview","volume":"16","author":"Baldi","year":"2000","journal-title":"Bioinformatics"},{"key":"ref_72","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_73","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.5194\/nhess-13-2815-2013","article-title":"Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues","volume":"13","author":"Catani","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_74","unstructured":"Freeman, E., Frescino, T., and Moisen, G. (2009). ModelMap: An R Package for Modeling and Map Production Using Random Forest and Stochastic Gradient Boosting, USDA Forest Service\/Rocky Mountain Research Station."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016). XGBoost, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining\u2014KDD \u201916, ACM Press.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chen, T., He, T., and Benesty, M. (2015). Xgboost: Extreme Gradient Boosting, Available online: http:\/\/cran.fhcrc.org\/web\/packages\/xgboost\/vignettes\/xgboost.pdf.","DOI":"10.32614\/CRAN.package.xgboost"},{"key":"ref_77","unstructured":"Bengio, Y., Lee, D.-H., Bornschein, J., Mesnard, T., and Lin, Z. (2015). Towards Biologically Plausible Deep Learning. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"94","DOI":"10.3389\/fncom.2016.00094","article-title":"Toward an Integration of Deep Learning and Neuroscience","volume":"10","author":"Marblestone","year":"2016","journal-title":"Front. Comput. Neurosci."},{"key":"ref_79","unstructured":"LeDell, E., Gill, N., Aiello, S., Fu, A., Candel, A., Click, C., Kraljevic, T., Nykodym, T., Aboyoun, P., and Kurka, M. (2018). H2O: R Interface for \u2018H2O\u2019, Available online: https:\/\/CRAN.R-project.org\/package=h2o."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Sandino, J., Pegg, G., Gonzalez, F., Smith, G., Sandino, J., Pegg, G., Gonzalez, F., and Smith, G. (2018). Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence. Sensors, 18.","DOI":"10.3390\/s18040944"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1007\/s12665-012-2181-2","article-title":"Landslide temporal analysis and susceptibility assessment as bases for landslide mitigation, Machu Picchu, Peru","volume":"70","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Shrestha, S., Kang, T.-S., and Suwal, M. (2017). An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6110365"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.catena.2018.12.018","article-title":"Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches","volume":"175","author":"Pham","year":"2019","journal-title":"CATENA"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.jhydrol.2011.03.051","article-title":"Height Above the Nearest Drainage\u2014A Hydrologically Relevant New Terrain Model","volume":"404","author":"Nobre","year":"2011","journal-title":"J. Hydrol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/S0013-7952(97)81260-4","article-title":"Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques","volume":"44","author":"Aksoy","year":"1996","journal-title":"Eng. Geol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1002\/jgrf.20099","article-title":"Detecting fingerprints of landslide drivers: A MaxEnt model","volume":"118","author":"Convertino","year":"2013","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10666-016-9538-y","article-title":"A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling","volume":"22","author":"Lagomarsino","year":"2017","journal-title":"Environ. Model. Assess."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.geomorph.2011.12.040","article-title":"GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China","volume":"145\u2013146","author":"Xu","year":"2012","journal-title":"Geomorphology"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1080\/10106049.2017.1323964","article-title":"Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea","volume":"33","author":"Kim","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1007\/s11069-015-1915-3","article-title":"Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: Application to the 2009 storm event in Messina (Sicily, southern Italy)","volume":"79","author":"Lombardo","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_91","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_92","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., Tiede, D., and Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Xiao, L., Zhang, Y., and Peng, G. (2018). Landslide susceptibility assessment using integrated deep learning algorithm along the china-nepal highway. Sensors (Switzerland), 18.","DOI":"10.3390\/s18124436"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Wang, F., Xu, P., Wang, C., Wang, N., and Jiang, N. (2017). Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6060172"},{"key":"ref_95","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_96","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_97","doi-asserted-by":"crossref","unstructured":"Ciregan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/10\/569\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:14:56Z","timestamp":1760177696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/10\/569"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,29]]},"references-count":97,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["ijgi9100569"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9100569","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202008.0089.v1","asserted-by":"object"}]},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,29]]}}}