{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T20:41:18Z","timestamp":1770496878435,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T00:00:00Z","timestamp":1701129600000},"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":["42071429"],"award-info":[{"award-number":["42071429"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["B17040"],"award-info":[{"award-number":["B17040"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 project","doi-asserted-by":"publisher","award":["42071429"],"award-info":[{"award-number":["42071429"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 project","doi-asserted-by":"publisher","award":["B17040"],"award-info":[{"award-number":["B17040"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide susceptibility mapping is typically based on binary prediction probabilities. However, non-landslide samples in modeling datasets are often unlabeled data, and the phenomenon of class-priori shift, that is, the proportion of landslide samples frequently deviates from real-world scenarios and is spatially heterogeneous. By comparing the classification performance and predicted probability distributions across multiple unbalanced datasets with known and unknown sample proportions, this study assesses the landslide susceptibility model\u2019s generalization ability in the context of class-prior shifts. The study investigates the potential of Bagging PU Learning, a semi-supervised learning approach, in improving the generalization performance of landslide susceptibility models and proposes the Bagging PU-GDBT algorithm. Our findings highlight the effectiveness of Bagging PU Learning in enhancing the recall of landslides and the generalization capabilities of models on unbalanced datasets. This method reduces prediction uncertainties, especially in high and very high susceptibility zones. Furthermore, results emphasize the superiority of models trained on balanced datasets with 1:1 sample ratio for landslide susceptibility mapping over those trained on unbalanced datasets.<\/jats:p>","DOI":"10.3390\/rs15235547","type":"journal-article","created":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T11:43:16Z","timestamp":1701171796000},"page":"5547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Comparative Study of Landslide Susceptibility Mapping Using Bagging PU Learning in Class-Prior Probability Shift Datasets"],"prefix":"10.3390","volume":"15","author":[{"given":"Lingran","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Hangling","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Jiahui","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Xueling","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Hang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Resources and Eco-Environment Geology, Hubei Geological Bureau, Wuhan 430000, China"},{"name":"Hubei Geological Environment Station, Wuhan 430000, China"}]},{"given":"Ruiqing","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1061\/(ASCE)GT.1943-5606.0000074","article-title":"Using TDR Cables and GPS for Landslide Monitoring in High Mountain Area","volume":"135","author":"Su","year":"2009","journal-title":"J. Geotech. Geoenviron. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, H., Li, C., Lu, G., Cai, Y., Zhang, J., and Tan, F. (2018). Design and Testing of a Flexible Inclinometer Probe for Model Tests of Landslide Deep Displacement Measurement. Sensors, 18.","DOI":"10.3390\/s18010224"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhu, H.-H., Shi, B., and Zhang, C.-C. (2017). FBG-Based Monitoring of Geohazards: Current Status and Trends. Sensors, 17.","DOI":"10.3390\/s17030452"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.cageo.2014.09.010","article-title":"2D Dry Granular Free-Surface Flow over Complex Topography with Obstacles. Part I: Experimental Study Using a Consumer-Grade RGB-D Sensor","volume":"73","author":"Juez","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cao, Y., Wei, X., Fan, W., Nan, Y., Xiong, W., and Zhang, S. (2021). Landslide Susceptibility Assessment Using the Weight of Evidence Method: A Case Study in Xunyang Area, China. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0245668"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2018.06.006","article-title":"Comparison of GIS-Based Landslide Susceptibility Models Using Frequency Ratio, Logistic Regression, and Artificial Neural Network in a Tertiary Region of Ambon, Indonesia","volume":"318","author":"Aditian","year":"2018","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1080\/13658816.2020.1808897","article-title":"A Comparative Study of Heterogeneous Ensemble-Learning Techniques for Landslide Susceptibility Mapping","volume":"35","author":"Fang","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.geomorph.2008.02.011","article-title":"Landslide Susceptibility Mapping Based on Support Vector Machine: A Case Study on Natural Slopes of Hong Kong, China","volume":"101","author":"Yao","year":"2008","journal-title":"Geomorphology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1080\/19479832.2021.1961316","article-title":"Landslide Susceptibility Mapping with the Fusion of Multi-Feature SVM Model Based FCM Sampling Strategy: A Case Study from Shaanxi Province","volume":"12","author":"Liu","year":"2021","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.enggeo.2008.01.004","article-title":"An Assessment on the Use of Logistic Regression and Artificial Neural Networks with Different Sampling Strategies for the Preparation of Landslide Susceptibility Maps","volume":"97","author":"Nefeslioglu","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1038\/s41598-023-28991-5","article-title":"An Objective Absence Data Sampling Method for Landslide Susceptibility Mapping","volume":"13","author":"Rabby","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1080\/17499518.2022.2088802","article-title":"Improved Landslide Susceptibility Mapping Using Unsupervised and Supervised Collaborative Machine Learning Models","volume":"17","author":"Su","year":"2023","journal-title":"Georisk Assess. Manag. Risk Eng. Syst. Geohazards"},{"key":"ref_16","first-page":"156","article-title":"Landslide Susceptibility Assessment Based on Clustering Analysis and Support Vector Machine","volume":"37","author":"Huang","year":"2018","journal-title":"Chin. J. Rock Mech. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2213807","DOI":"10.1080\/19475705.2023.2213807","article-title":"A LightGBM-Based Landslide Susceptibility Model Considering the Uncertainty of Non-Landslide Samples","volume":"14","author":"Sun","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"11581","DOI":"10.1109\/JSTARS.2021.3125741","article-title":"Landslide Susceptibility Prediction Based on Positive Unlabeled Learning Coupled With Adaptive Sampling","volume":"14","author":"Fang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1109\/LGRS.2020.2989497","article-title":"Landslide Susceptibility Modeling Using Bagging-Based Positive-Unlabeled Learning","volume":"18","author":"Wu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Elkan, C., and Noto, K. (2008, January 24). Learning Classifiers from Only Positive and Unlabeled Data. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, NV, USA.","DOI":"10.1145\/1401890.1401920"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1007\/s10994-022-06190-z","article-title":"Positive-Unlabeled Classification under Class-Prior Shift: A Prior-Invariant Approach Based on Density Ratio Estimation","volume":"112","author":"Nakajima","year":"2023","journal-title":"Mach. Learn."},{"key":"ref_22","unstructured":"Li, X., and Liu, B. (2003, January 9\u201315). Learning to Classify Texts Using Positive and Unlabeled Data. Proceedings of the 18th International Joint Conference on Artificial Intelligence, Acapulco, Mexico."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, H., Han, J., and Chang, K.C.-C. (2002, January 23). PEBL: Positive Example Based Learning for Web Page Classification Using SVM. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, AB, Canada.","DOI":"10.1145\/775047.775083"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5823","DOI":"10.1038\/s41598-023-33186-z","article-title":"Comparative Study on Landslide Susceptibility Mapping Based on Unbalanced Sample Ratio","volume":"13","author":"Tang","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104425","DOI":"10.1016\/j.catena.2019.104425","article-title":"A Hybrid Model Considering Spatial Heterogeneity for Landslide Susceptibility Mapping in Zhejiang Province, China","volume":"188","author":"Wang","year":"2020","journal-title":"Catena"},{"key":"ref_26","unstructured":"Deng, W. (2011). The Multi-Fractal of the Spatial Distribution of Landslide, Chongqing Normal University."},{"key":"ref_27","unstructured":"Wright, R. (2017). Positive-Unlabeled Learning."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101425","DOI":"10.1016\/j.gsf.2022.101425","article-title":"Multi-Hazard Susceptibility Mapping Based on Convolutional Neural Networks","volume":"13","author":"Ullah","year":"2022","journal-title":"Geosci. Front."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106428","DOI":"10.1016\/j.catena.2022.106428","article-title":"Identifying the Essential Conditioning Factors of Landslide Susceptibility Models under Different Grid Resolutions Using Hybrid Machine Learning: A Case of Wushan and Wuxi Counties, China","volume":"217","author":"Liao","year":"2022","journal-title":"Catena"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3045","DOI":"10.1007\/s11069-021-04812-8","article-title":"Assessment of Landslide Susceptibility and Risk Factors in China","volume":"108","author":"Wang","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.patrec.2013.06.010","article-title":"A Bagging SVM to Learn from Positive and Unlabeled Examples","volume":"37","author":"Mordelet","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_32","first-page":"464","article-title":"Novelty Detection: Unlabeled Data Definitely Help","volume":"Volume 5","author":"Welling","year":"2009","journal-title":"Artificial Intelligence and Statistics, Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, Clearwater Beach, FL, USA, 15 April 2009"},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","article-title":"Receiver Operating Characteristic Curve in Diagnostic Test Assessment","volume":"5","author":"Mandrekar","year":"2010","journal-title":"J. Thorac. Oncol."},{"key":"ref_36","unstructured":"Kouw, W.M., and Loog, M. (2018). An Introduction to Domain Adaptation and Transfer Learning. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.geomorph.2006.04.007","article-title":"Estimating the Quality of Landslide Susceptibility Models","volume":"81","author":"Guzzetti","year":"2006","journal-title":"Geomorphology"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.1080\/19475705.2020.1803421","article-title":"Using the Rotation and Random Forest Models of Ensemble Learning to Predict Landslide Susceptibility","volume":"11","author":"Zhao","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.gr.2022.05.012","article-title":"Machine Learning-Based Landslide Susceptibility Assessment with Optimized Ratio of Landslide to Non-Landslide Samples","volume":"123","author":"Yang","year":"2023","journal-title":"Gondwana Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10064-020-01969-7","article-title":"Comparative Landslide Spatial Research Based on Various Sample Sizes and Ratios in Penang Island, Malaysia","volume":"80","author":"Gao","year":"2021","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1007\/s12583-020-1072-9","article-title":"An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China","volume":"31","author":"Sun","year":"2020","journal-title":"J. Earth Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chu, H.-J., Chen, Y.-C., Ali, M., and H\u00f6fle, B. (2019). Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16071109"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8225322","DOI":"10.1155\/2022\/8225322","article-title":"Utilization of 3D Laser Scanning for Stability Evaluation and Deformation Monitoring of Landslides","volume":"2022","author":"Guo","year":"2022","journal-title":"J. Environ. Public Health"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s41651-023-00138-0","article-title":"Novel Landslide Susceptibility Mapping Based on Multi-Criteria Decision-Making in Ouro Preto, Brazil","volume":"7","author":"Mantovani","year":"2023","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5029","DOI":"10.1007\/s10706-022-02197-4","article-title":"GIS-Based AHP and FR Methods for Landslide Susceptibility Mapping in the Abay Gorge, Dejen\u2013Renaissance Bridge, Central, Ethiopia","volume":"40","author":"Tesfa","year":"2022","journal-title":"Geotech. Geol. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.5194\/nhess-23-1191-2023","article-title":"Rainfall Thresholds Estimation for Shallow Landslides in Peru from Gridded Daily Data","volume":"23","year":"2023","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"117357","DOI":"10.1016\/j.jenvman.2023.117357","article-title":"Insights into Geospatial Heterogeneity of Landslide Susceptibility Based on the SHAP-XGBoost Model","volume":"332","author":"Zhang","year":"2023","journal-title":"J. Environ. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106649","DOI":"10.1016\/j.catena.2022.106649","article-title":"Evaluating the Post-Earthquake Landslides Sediment Supply Capacity for Debris Flows","volume":"220","author":"Jin","year":"2023","journal-title":"Catena"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Carri\u00f3n-Mero, P., Montalv\u00e1n-Burbano, N., Morante-Carballo, F., Quesada-Rom\u00e1n, A., and Apolo-Masache, B. (2021). Worldwide Research Trends in Landslide Science. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18189445"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Sassa, K., Konagai, K., Tiwari, B., Arbanas, \u017d., and Sassa, S. (2023). Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022, Springer International Publishing. Progress in Landslide Research and Technology.","DOI":"10.1007\/978-3-031-16898-7"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"105842","DOI":"10.1016\/j.landusepol.2021.105842","article-title":"Evaluating the Use of the Landslide Database in Spatial Planning in Mountain Communes (the Polish Carpathians)","volume":"112","author":"Izdebski","year":"2022","journal-title":"Land Use Policy"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1007\/s10346-022-01870-2","article-title":"Fatal Landslides in Colombia (from Historical Times to 2020) and Their Socio-Economic Impacts","volume":"19","author":"Petley","year":"2022","journal-title":"Landslides"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5547\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:32:53Z","timestamp":1760131973000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/23\/5547"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,28]]},"references-count":53,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["rs15235547"],"URL":"https:\/\/doi.org\/10.3390\/rs15235547","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,28]]}}}