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However, the problem of sample imbalance in landslide susceptibility assessment, where landslide samples tend to be much smaller than non-landslide samples, is often overlooked. This problem is often one of the important factors affecting the performance of landslide susceptibility models. In this paper, we take the Wanzhou district of Chongqing city as an example, where the total number of data sets is more than 580,000 and the ratio of positive to negative samples is 1:19. We oversample or undersample the unbalanced landslide samples to make them balanced, and then compare the performance of machine learning models with different sampling strategies. Three classic machine learning algorithms, logistic regression, random forest and LightGBM, are used for LSA modeling. The results show that the model trained directly using the unbalanced sample dataset performs the worst, showing an extremely low recall rate, indicating that its predictive ability for landslide samples is extremely low and cannot be applied in practice. Compared with the original dataset, the sample set optimized through certain methods has demonstrated improved predictive performance across various classifiers, manifested in the improvement of AUC value and recall rate. The best model was the random forest model using over-sampling (O_RF) (AUC = 0.932).<\/jats:p>","DOI":"10.3390\/ijgi12050197","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T08:28:16Z","timestamp":1684139296000},"page":"197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5080-9057","authenticated-orcid":false,"given":"Yingze","family":"Song","sequence":"first","affiliation":[{"name":"Institute of Computer and Information Science, Chongqing Normal University, Chongqing 400047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8582-4302","authenticated-orcid":false,"given":"Degang","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Computer and Information Science, Chongqing Normal University, Chongqing 400047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6362-9840","authenticated-orcid":false,"given":"Weicheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Computer and Information Science, Chongqing Normal University, Chongqing 400047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Computer and Information Science, Chongqing Normal University, Chongqing 400047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoxu","family":"Tian","sequence":"additional","affiliation":[{"name":"Institute of Computer and Information Science, Chongqing Normal University, Chongqing 400047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chencan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Computer and Information Science, Chongqing Normal University, Chongqing 400047, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9273-2019","authenticated-orcid":false,"given":"Yingxu","family":"Song","sequence":"additional","affiliation":[{"name":"Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, Nanchang 330013, China"},{"name":"School of Information and Engineering, East China University of Technology, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"24112","DOI":"10.1038\/s41598-021-03585-1","article-title":"Deep learning-based landslide susceptibility mapping","volume":"11","author":"Azarafza","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Nikoobakht, S., Azarafza, M., Akg\u00fcn, H., and Derakhshani, R. (2022). Landslide susceptibility assessment by using convolutional neural network. Appl. Sci., 12.","DOI":"10.3390\/app12125992"},{"key":"ref_4","first-page":"175","article-title":"Machine learning for predicting landslide risk of Rohingya refugee camp infrastructure","volume":"4","author":"Ahmed","year":"2020","journal-title":"J. Inf. Telecommun."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.catena.2017.05.016","article-title":"A comparative study between popular statistical and machine learning methods for simulating volume of landslides","volume":"157","author":"Shirzadi","year":"2017","journal-title":"Catena"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1007\/s11069-022-05423-7","article-title":"Machine learning and landslide studies: Recent advances and applications","volume":"114","author":"Tehrani","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xu, S., Song, Y., and Hao, X. (2022). A Comparative Study of Shallow Machine Learning Models and Deep Learning Models for Landslide Susceptibility Assessment Based on Imbalanced Data. Forests, 13.","DOI":"10.3390\/f13111908"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.cageo.2013.04.006","article-title":"Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy","volume":"57","author":"Wang","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_9","first-page":"407","article-title":"Fuzzy-based multiple decision method for landslide susceptibility and hazard assessment: A case study of Tabriz, Iran","volume":"24","author":"Nanehkaran","year":"2021","journal-title":"Geomech. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-018-7978-1","article-title":"Landslide susceptibility assessment of South Pars Special Zone, southwest Iran","volume":"77","author":"Azarafza","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"399","DOI":"10.3390\/geomatics1040023","article-title":"Entropy-based hybrid integration of random forest and support vector machine for landslide susceptibility analysis","volume":"1","author":"Sharma","year":"2021","journal-title":"Geomatics"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, S., Wang, Y., and Wu, G. (2022). Earthquake-Induced Landslide Susceptibility Assessment Using a Novel Model Based on Gradient Boosting Machine Learning and Class Balancing Methods. Remote Sens., 14.","DOI":"10.3390\/rs14235945"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/s10346-022-01998-1","article-title":"Handling data imbalance in machine learning based landslide susceptibility mapping: A case study of Mandakini River Basin, North-Western Himalayas","volume":"20","author":"Gupta","year":"2022","journal-title":"Landslides"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, X., Chen, Z., Ren, F., Feng, L., and Du, Q. (2019). Optimizing the predictive ability of machine learning methods for landslide susceptibility mapping using SMOTE for Lishui City in Zhejiang Province, China. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16030368"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Shamsudin, H., Yusof, U.K., Jayalakshmi, A., and Khalid, M.N.A. (2020, January 9\u201311). Combining oversampling and undersampling techniques for imbalanced classification: A comparative study using credit card fraudulent transaction dataset. Proceedings of the 2020 IEEE 16th International Conference on Control & Automation (ICCA), Sapporo, Japan.","DOI":"10.1109\/ICCA51439.2020.9264517"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yap, B.W., Abd Rani, K., Abd Rahman, H.A., Fong, S., Khairudin, Z., and Abdullah, N.N. (2014, January 3\u20135). An Application of Oversampling, Undersampling, Bagging and Boosting in Handling Imbalanced Datasets. Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013), Kuala Lumpur, Malaysia.","DOI":"10.1007\/978-981-4585-18-7_2"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Junsomboon, N., and Phienthrakul, T. (2017, January 24\u201326). Combining over-sampling and under-sampling techniques for imbalance dataset. Proceedings of the 9th International Conference on Machine Learning and Computing, Singapore.","DOI":"10.1145\/3055635.3056643"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"104966","DOI":"10.1016\/j.cageo.2021.104966","article-title":"Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China","volume":"158","author":"Zhang","year":"2022","journal-title":"Comput. Geosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S0013-7952(03)00142-X","article-title":"Determination and application of the weights for landslide susceptibility mapping using an artificial neural network","volume":"71","author":"Lee","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s11069-011-9879-4","article-title":"Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania","volume":"60","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L","article-title":"The relationship between recall and precision","volume":"45","author":"Buckland","year":"1994","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102637","DOI":"10.1016\/j.dsp.2019.102637","article-title":"G-mean based extreme learning machine for imbalance learning","volume":"98","author":"Ri","year":"2020","journal-title":"Digit. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1007\/s11629-013-2498-7","article-title":"Reservoir-landslide hazard assessment based on GIS: A case study in Wanzhou section of the Three Gorges Reservoir","volume":"10","author":"Wang","year":"2013","journal-title":"J. Mt. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1007\/s11069-021-04898-0","article-title":"Ancient landslide in Wanzhou District analysis from 2015 to 2018 based on ALOS-2 data by QPS-InSAR","volume":"109","author":"Huang","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0013-7952(00)00061-2","article-title":"Zonation of the landslide hazards in the forereservoir region of the Three Gorges Project on the Yangtze River","volume":"59","author":"Wu","year":"2001","journal-title":"Eng. Geol."},{"key":"ref_27","first-page":"3159","article-title":"Study on Ji\u2019an landslide characters and origin in Wanzhou Chongqing","volume":"24","author":"Li","year":"2005","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Song, Y., Niu, R., Xu, S., Ye, R., Peng, L., Guo, T., Li, S., and Chen, T. (2018). 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_29","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_30","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s11069-012-0217-2","article-title":"Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran","volume":"63","author":"Pourghasemi","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s00366-018-0644-0","article-title":"Modification of landslide susceptibility mapping using optimized PSO-ANN technique","volume":"35","author":"Moayedi","year":"2019","journal-title":"Eng. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1029\/93WR02979","article-title":"A physically based model for the topographic control on shallow landsliding","volume":"30","author":"Montgomery","year":"1994","journal-title":"Water Resour. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/19475705.2017.1407368","article-title":"Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)","volume":"9","author":"Kalantar","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s10346-020-01444-0","article-title":"Dynamic development of landslide susceptibility based on slope unit and deep neural networks","volume":"18","author":"Hua","year":"2021","journal-title":"Landslides"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s11069-012-0523-8","article-title":"PROMETHEE II and fuzzy AHP: An enhanced GIS-based landslide susceptibility mapping","volume":"73","author":"Roodposhti","year":"2014","journal-title":"Nat. Hazards"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gong, W., Hu, M., Zhang, Y., Tang, H., Liu, D., and Song, Q. (2021). GIS-Based Landslide Susceptibility Mapping using Ensemble Methods for Fengjie County in the Three Gorges Reservoir Region, China, Springer.","DOI":"10.1007\/s13762-021-03572-z"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1023\/B:NHAZ.0000007097.42735.9e","article-title":"Use of geomorphological information in indirect landslide susceptibility assessment","volume":"30","author":"Rengers","year":"2003","journal-title":"Nat. Hazards"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cheng, J., Dai, X., Wang, Z., Li, J., Qu, G., Li, W., She, J., and Wang, Y. (2022). Landslide susceptibility assessment model construction using typical machine learning for the Three Gorges Reservoir Area in China. Remote Sens., 14.","DOI":"10.3390\/rs14092257"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhang, Y., and Zhao, Y. (2017, January 18\u201320). LightGBM: An effective miRNA classification method in breast cancer patients. Proceedings of the 2017 International Conference on Computational Biology and Bioinformatics, Newark, NJ, USA.","DOI":"10.1145\/3155077.3155079"},{"key":"ref_42","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."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1214\/aos\/1176345638","article-title":"Bootstrapping regression models","volume":"9","author":"Freedman","year":"1981","journal-title":"Ann. Stat."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhang, Q., Singh, V.P., Wang, G., Zhao, J., Shen, Z., and Sun, S. (2022). A Data-Driven Model on Google Earth Engine for Landslide Susceptibility Assessment in the Hengduan Mountains, the Qinghai\u2013Tibetan Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14184662"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.patrec.2018.01.003","article-title":"Oversampling imbalanced data in the string space","volume":"103","author":"Castellanos","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","unstructured":"Liu, A., Ghosh, J., and Martin, C. (2007, January 23\u201330). Generative Oversampling for Mining Imbalanced Datasets. Proceedings of the DMIN, Prague, Czech Republic."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1613\/jair.1.11192","article-title":"SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary","volume":"61","author":"Garcia","year":"2018","journal-title":"J. Artif. Intell. Res."},{"key":"ref_49","first-page":"539","article-title":"Exploratory undersampling for class-imbalance learning","volume":"39","author":"Liu","year":"2008","journal-title":"IEEE Trans. Syst. Man, Cybern. Part B"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Brodersen, K.H., Ong, C.S., Stephan, K.E., and Buhmann, J.M. (2010, January 23\u201326). The balanced accuracy and its posterior distribution. Proceedings of the 2010 20th International Conference on Pattern Recognition, Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.764"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TKDE.2005.50","article-title":"Using AUC and accuracy in evaluating learning algorithms","volume":"17","author":"Huang","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1007\/s13042-020-01090-x","article-title":"Extreme learning machine with hybrid cost function of G-mean and probability for imbalance learning","volume":"11","author":"Ri","year":"2020","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/j.1466-8238.2007.00358.x","article-title":"AUC: A misleading measure of the performance of predictive distribution models","volume":"17","author":"Lobo","year":"2008","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"40","DOI":"10.3758\/BF03213026","article-title":"Theoretical analysis of an alphabetic confusion matrix","volume":"9","author":"Townsend","year":"1971","journal-title":"Percept. Psychophys."},{"key":"ref_56","first-page":"120","article-title":"Confusion matrix-based feature selection","volume":"710","author":"Visa","year":"2011","journal-title":"Maics"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TII.2019.2943898","article-title":"Deep residual shrinkage networks for fault diagnosis","volume":"16","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/5\/197\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:34:13Z","timestamp":1760124853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/12\/5\/197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,13]]},"references-count":58,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["ijgi12050197"],"URL":"https:\/\/doi.org\/10.3390\/ijgi12050197","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,13]]}}}