{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T02:16:33Z","timestamp":1783476993735,"version":"3.55.0"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,25]],"date-time":"2018-12-25T00:00:00Z","timestamp":1545696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Geological Survey Project (No. 0431203), the Three Gorges Follow-up Work on Geological Disaster Prevention and Research Project, National Natural Science Foundation of China","award":["0001212018CC60010, 0001122012AC50021,41602362"],"award-info":[{"award-number":["0001212018CC60010, 0001122012AC50021,41602362"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The main goal of this study is to produce a landslide susceptibility map in the Wanzhou section of the Three Gorges reservoir area (China) with a weighted gradient boosting decision tree (weighted GBDT) model. According to the current research on landslide susceptibility mapping (LSM), the GBDT method is rarely used in LSM. Furthermore, previous studies have rarely considered the imbalance of landslide samples and simply regarded the LSM problem as a binary classification problem. In this paper, we considered LSM as an imbalanced learning problem and obtained a better predictive model using the weighted GBDT method. The innovations of the article mainly include the following two points: introducing the GBDT model into the evaluation of landslide susceptibility; using the weighted GBDT method to deal with the problem of landslide sample imbalance. The logistic regression (LR) model and gradient boosting decision tree (GBDT) model were also used in the study to compare with the weighted GBDT model. Five kinds of data from different data source were used in the study: geology, topography, hydrology, land cover, and triggered factors (rainfall, earthquake, land use, etc.). Twenty nine environmental parameters and 233 landslides were used as input data. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) value, and the recall value were used to estimate the quality of the weighted GBDT model, the GBDT model, and the LR model. The results showed that the GBDT model and the weighted GBDT model had a higher AUC value (0.977, 0.976) than the LR model (0.845); the weighted GBDT model had a little higher AUC value (0.977) than the GBDT model (0.976); and the weighted GBDT model had a higher recall value (0.823) than the GBDT model (0.426) and the LR model (0.004). The weighted GBDT method could be considered to have the best performance considering the AUC value and the recall value in landslide susceptibility mapping dealing with imbalanced landslide data.<\/jats:p>","DOI":"10.3390\/ijgi8010004","type":"journal-article","created":{"date-parts":[[2018,12,26]],"date-time":"2018-12-26T04:29:54Z","timestamp":1545798594000},"page":"4","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":96,"title":["Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China)"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9273-2019","authenticated-orcid":false,"given":"Yingxu","family":"Song","sequence":"first","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Central-south China Geoscience Innovation Center, China Geological Survey, Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiqing","family":"Niu","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiluo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Huzhou University, Huzhou 313000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runqing","family":"Ye","sequence":"additional","affiliation":[{"name":"Wuhan Geological Survey Center, China Geological Survey, Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Peng","sequence":"additional","affiliation":[{"name":"China Institute of Geo-Environment Monitoring, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0587-4762","authenticated-orcid":false,"given":"Tao","family":"Guo","sequence":"additional","affiliation":[{"name":"Sichuan Zhitu Information Technology Co., Ltd., Chengdu 610000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6965-1256","authenticated-orcid":false,"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1130\/G33217.1","article-title":"Global patterns of loss of life from landslides","volume":"40","author":"Petley","year":"2012","journal-title":"Geology"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s12517-018-3531-5","article-title":"Analysis and evaluation of landslide susceptibility: A review on articles published during 2005\u20132016 (periods of 2005\u20132012 and 2013\u20132016)","volume":"11","author":"Pourghasemi","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1007\/s12665-016-5919-4","article-title":"GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks","volume":"75","author":"Bui","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_5","unstructured":"Trigila, A., Frattini, P., Casagli, N., Catani, F., Crosta, G., Esposito, C., Iadanza, C., Lagomarsino, D., Mugnozza, G.S., and Segoni, S. (2015). Landslide Susceptibility Mapping at National Scale: The Italian Case Study. Landslide Science and Practice, Springer."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1080\/13658816.2012.693614","article-title":"GIS techniques for regional-scale landslide susceptibility assessment: The Sicily (Italy) case study","volume":"27","author":"Manzo","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1007\/s00254-001-0454-2","article-title":"Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach","volume":"41","author":"Ercanoglu","year":"2002","journal-title":"Environ. Geol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.geomorph.2014.02.003","article-title":"An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic","volume":"214","author":"Zhu","year":"2014","journal-title":"Geomorphology"},{"key":"ref_9","unstructured":"Zhu, A., Wang, R.X., Qiao, J.P., Chen, Y.B., Cai, Q.G., Zhou, C.H., Chen, Y., Takara, K., Cluckie, I.D., and Smedt, H.F.D. (2003). Mapping Landslide Susceptibility in the Three Gorges Area, China Using GIS, Expert Knowledge and Fuzzy Logic, Iahs Publication."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, C. (2015, January 12\u201317). The comparison of landslide ratio-based and general logistic regression landslide susceptibility models in the Chishan watershed after 2009 Typhoon Morakot. Proceedings of the EGU General Assembly Conference, Vienna, Austria.","DOI":"10.1007\/s11629-014-3416-3"},{"key":"ref_11","unstructured":"Wang, R. (2008). An Expert Knowledge-Based Approach to Landslide Susceptibility Mapping Using Gis and Fuzzy Logic, University of Wisconsin at Madison."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-015-4028-0","article-title":"Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran","volume":"73","author":"Shahabi","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s12665-010-0705-1","article-title":"Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia","volume":"63","author":"Pradhan","year":"2011","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s12524-010-0020-z","article-title":"Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches","volume":"38","author":"Pradhan","year":"2010","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s11707-012-0337-8","article-title":"Application of fuzzy logic approach for landslide susceptibility mapping in Garuwa sub-basin, East Nepal","volume":"6","author":"Kayastha","year":"2012","journal-title":"Front. Earth Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.cageo.2012.11.003","article-title":"Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal","volume":"52","author":"Kayastha","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s12665-012-1842-5","article-title":"Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea","volume":"68","author":"Park","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1007\/s11069-013-0728-5","article-title":"Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances","volume":"69","author":"Pourghasemi","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s12665-016-5317-y","article-title":"A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS","volume":"75","author":"Chen","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.jseaes.2012.12.014","article-title":"A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey","volume":"64","author":"Ozdemir","year":"2013","journal-title":"J. Asian Earth Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2557","DOI":"10.1007\/s12517-012-0526-5","article-title":"Landslide susceptibility mapping based on frequency ratio and logistic regression models","volume":"6","author":"Solaimani","year":"2013","journal-title":"Arab. J. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1007\/s10346-010-0202-3","article-title":"Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model","volume":"7","author":"Chauhan","year":"2010","journal-title":"Landslides"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s10346-011-0283-7","article-title":"A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: A case study at \u0130zmir, Turkey","volume":"9","author":"Akgun","year":"2012","journal-title":"Landslides"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s12665-010-0509-3","article-title":"GIS-based rare events logistic regression for landslide-susceptibility mapping of Lianyungang, China","volume":"62","author":"Bai","year":"2011","journal-title":"Environ. Earth Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/j.geomorph.2009.09.023","article-title":"Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)","volume":"114","author":"Das","year":"2010","journal-title":"Geomorphology"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.catena.2016.06.004","article-title":"Comparison of a logistic regression and Na\u00efve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size","volume":"145","author":"Tsangaratos","year":"2016","journal-title":"Catena"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2004.10.004","article-title":"Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela","volume":"78","author":"Kavzoglu","year":"2005","journal-title":"Eng. Geol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.geomorph.2004.09.025","article-title":"Artificial Neural Networks applied to landslide susceptibility assessment","volume":"66","author":"Ermini","year":"2005","journal-title":"Geomorphology"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.envsoft.2009.10.016","article-title":"Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling","volume":"25","author":"Pradhan","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yao, X., Zhang, Y., Zhou, N., Guo, C., Yu, K., and Li, L.J. (2013, January 24\u201325). Application of two-class SVM applied in landslide susceptibility mapping. Proceedings of the International Symposium and 9th Asian Regional Conference of IAEG, Beijing, China.","DOI":"10.1201\/b15794-34"},{"key":"ref_32","first-page":"487","article-title":"Research on the Method to Select Landslide Susceptibility Evaluation Factors Based on RS-SVM Model","volume":"13","author":"Niu","year":"2016","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.enggeo.2011.09.006","article-title":"Landslide susceptibility assessment using SVM machine learning algorithm","volume":"123","author":"Bajat","year":"2011","journal-title":"Eng. Geol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.geomorph.2013.08.013","article-title":"Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China","volume":"204","author":"Peng","year":"2014","journal-title":"Geomorphology"},{"key":"ref_35","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_36","first-page":"991","article-title":"Landslide hazard evaluation of Wanzhou based on GIS information value method in the Three Gorges Reservoir","volume":"25","author":"Gao","year":"2006","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2017.04.013","article-title":"Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine","volume":"223","author":"Huang","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_38","first-page":"797","article-title":"Landslide susceptibility assessment based on GIS and weighted information value: A case study of Wanzhou district, Three Gorges Reservoir","volume":"33","author":"Wang","year":"2014","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, X., Wang, Y., Niu, R., and Hu, Y. (2016). A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China. Int. J. Environ. Res. Public Health, 13.","DOI":"10.3390\/ijerph13050487"},{"key":"ref_40","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dou, J., Tien, B.D., Yunus, A.P., Jia, K., Song, X., Revhaug, I., Xia, H., and Zhu, Z. (2015). Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0133262"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10346-009-0188-x","article-title":"Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway)","volume":"7","author":"Erener","year":"2010","journal-title":"Landslides"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1080\/01431160412331331012","article-title":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data","volume":"26","author":"Lee","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.asoc.2014.05.023","article-title":"Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection","volume":"22","author":"Verbiest","year":"2014","journal-title":"Appl. Soft Comput. J."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, S., Liang, Y., Ma, L., and He, Y. (2010, January 28\u201330). MSMOTE: Improving Classification Performance When Training Data is Imbalanced. Proceedings of the 2009 Second International Workshop on Computer Science and Engineering, Qingdao, China.","DOI":"10.1109\/WCSE.2009.756"},{"key":"ref_48","first-page":"544","article-title":"Ensemble-Based Wrapper Methods for Feature Selection and Class Imbalance Learning","volume":"Volume 7818","author":"Yang","year":"2013","journal-title":"Pacific-Asia Conference on Knowledge Discovery and Data Mining"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0167-8809(01)00187-6","article-title":"Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA","volume":"85","author":"Pontius","year":"2001","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10980-013-9984-8","article-title":"Recommendations for using the relative operating characteristic (ROC)","volume":"29","author":"Pontius","year":"2014","journal-title":"Landsc. Ecol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"Catena"},{"key":"ref_52","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":"Bui","year":"2016","journal-title":"Landslides"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yang, J., Song, C., Yang, Y., Xu, C., Guo, F., and Xie, L. (2018). New method for landslide susceptibility mapping supported by spatial logistic regression and GeoDetector: A case study of Duwen Highway Basin, Sichuan Province, China. Geomorphology.","DOI":"10.1016\/j.geomorph.2018.09.019"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/1\/4\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:36:05Z","timestamp":1760196965000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/1\/4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,25]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["ijgi8010004"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8010004","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,25]]}}}