{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T15:19:29Z","timestamp":1773069569289,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42230715"],"award-info":[{"award-number":["42230715"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate prediction of landslide susceptibility relies on effectively handling absence samples in data-driven models. This study investigates the influence of different absence sampling methods, including buffer control sampling (BCS), controlled target space exteriorization sampling (CTSES), information value (IV), and mini-batch k-medoids (MBKM), on landslide susceptibility mapping in Songyang County, China, using support vector machines and random forest algorithms. Various evaluation metrics are employed to compare the efficacy of these sampling methods for susceptibility zoning. The results demonstrate that CTSES, IV, and MBKM methods exhibit an expansion of the high susceptibility region (maximum susceptibility mean value reaching 0.87) and divergence in the susceptibility index when extreme absence samples are present, with MBKM showing a comparative advantage (lower susceptibility mean value) compared to the IV model. Building on the strengths of different sampling methods, a novel integrative sampling approach that incorporates multiple existing methods is proposed. The integrative sampling can mitigate negative effects caused by extreme absence samples (susceptibility mean value is approximately 0.5 in the same extreme samples and presence-absence ratio) and obtain significantly better prediction results (AUC = 0.92, KC = 0.73, POA = 2.46 in the best model). Additionally, the mean level of susceptibility is heavily influenced by the proportion of absent samples.<\/jats:p>","DOI":"10.3390\/rs15133345","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:49:27Z","timestamp":1688345367000},"page":"3345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Enhanced Absence Sampling Technique for Data-Driven Landslide Susceptibility Mapping: A Case Study in Songyang County, China"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4269-5031","authenticated-orcid":false,"given":"Zijin","family":"Fu","sequence":"first","affiliation":[{"name":"Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China"}]},{"given":"Fawu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China"},{"name":"Key Laboratory of Geotechnical and Underground Engineering of the Ministry of Education, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5930-199X","authenticated-orcid":false,"given":"Jie","family":"Dou","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences-Wuhan, Wuhan 430074, China"}]},{"given":"Kounghoon","family":"Nam","sequence":"additional","affiliation":[{"name":"Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9685-4809","authenticated-orcid":false,"given":"Hao","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.pce.2009.07.006","article-title":"Landslide Simulation by a Geotechnical Model Combined with a Model for Apparent Friction Change","volume":"35","author":"Wang","year":"2010","journal-title":"Phys. Chem. Earth Parts ABC"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Adnan, M.S.G., Rahman, M.S., Ahmed, N., Ahmed, B., Rabbi, M.F., and Rahman, R.M. (2020). Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping. Remote Sens., 12.","DOI":"10.3390\/rs12203347"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhao, C., and Lu, Z. (2018). Remote Sensing of Landslides\u2014A Review. Remote Sens., 10.","DOI":"10.3390\/rs10020279"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e3998","DOI":"10.1002\/ett.3998","article-title":"Review on Remote Sensing Methods for Landslide Detection Using Machine and Deep Learning","volume":"32","author":"Mohan","year":"2021","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.geomorph.2005.06.002","article-title":"Probabilistic Landslide Hazard Assessment at the Basin Scale","volume":"72","author":"Guzzetti","year":"2005","journal-title":"Geomorphology"},{"key":"ref_7","first-page":"209","article-title":"Recommendations for the Quantitative Analysis of Landslide Risk","volume":"73","author":"Corominas","year":"2014","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10346-016-0771-x","article-title":"A Modified Frequency Ratio Method for Landslide Susceptibility Assessment","volume":"14","author":"Li","year":"2017","journal-title":"Landslides"},{"key":"ref_9","unstructured":"Dou, J., Xiang, Z., Qiang, X., Zheng, P., Wang, X., Su, A., Liu, J., and Luo, W. (2022). Application and Development Trend of Machine Learning in Landslide Intelligent Disaster Prevention and Mitigation. Earth Sci."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40677-020-0143-7","article-title":"An Extreme Rainfall-Induced Landslide Susceptibility Assessment Using Autoencoder Combined with Random Forest in Shimane Prefecture, Japan","volume":"7","author":"Nam","year":"2020","journal-title":"Geoenvironmental Disasters"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s40677-019-0137-5","article-title":"The Performance of Using an Autoencoder for Prediction and Susceptibility Assessment of Landslides: A Case Study on Landslides Triggered by the 2018 Hokkaido Eastern Iburi Earthquake in Japan","volume":"6","author":"Nam","year":"2019","journal-title":"Geoenvironmental Disasters"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10346-019-01274-9","article-title":"A Deep Learning Algorithm Using a Fully Connected Sparse Autoencoder Neural Network for Landslide Susceptibility Prediction","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104580","DOI":"10.1016\/j.catena.2020.104580","article-title":"Comparisons of Heuristic, General Statistical and Machine Learning Models for Landslide Susceptibility Prediction and Mapping","volume":"191","author":"Huang","year":"2020","journal-title":"Catena"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, F., Xiong, H., Yao, C., Catani, F., Zhou, C., and Huang, J. (2023). Uncertainties of Landslide Susceptibility Prediction Considering Different Landslide Types. J. Rock Mech. Geotech. Eng.","DOI":"10.1016\/j.jrmge.2023.03.001"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.catena.2018.07.012","article-title":"Comparison of the Presence-Only Method and Presence-Absence Method in Landslide Susceptibility Mapping","volume":"171","author":"Zhu","year":"2018","journal-title":"Catena"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S0098-3004(97)00117-9","article-title":"Generalised linear modelling of susceptibility to landsliding in the central apennines, Italy","volume":"24","author":"Atkinson","year":"1998","journal-title":"Comput. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5811","DOI":"10.1007\/s10064-019-01506-1","article-title":"Landslide Susceptibility Mapping: A Practitioner\u2019s View","volume":"78","author":"Hearn","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Carrara, A., and Guzzetti, F. (1995). Geographical Information Systems in Assessing Natural Hazards, Advances in Natural and Technological Hazards Research; Springer.","DOI":"10.1007\/978-94-015-8404-3"},{"key":"ref_20","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_21","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_22","doi-asserted-by":"crossref","first-page":"104188","DOI":"10.1016\/j.catena.2019.104188","article-title":"A Similarity-Based Approach to Sampling Absence Data for Landslide Susceptibility Mapping Using Data-Driven Methods","volume":"183","author":"Zhu","year":"2019","journal-title":"Catena"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"105067","DOI":"10.1016\/j.catena.2020.105067","article-title":"Investigation of the Influence of Nonoccurrence Sampling on Landslide Susceptibility Assessment Using Artificial Neural Networks","volume":"198","author":"Lucchese","year":"2021","journal-title":"Catena"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.compenvurbsys.2009.12.004","article-title":"A GIS-Based Back-Propagation Neural Network Model and Its Cross-Application and Validation for Landslide Susceptibility Analyses","volume":"34","author":"Pradhan","year":"2010","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106103","DOI":"10.1016\/j.enggeo.2021.106103","article-title":"AI-Powered Landslide Susceptibility Assessment in Hong Kong","volume":"288","author":"Wang","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.gr.2023.02.007","article-title":"Uncertainty Analysis of Non-Landslide Sample Selection in Landslide Susceptibility Prediction Using Slope Unit-Based Machine Learning Models","volume":"117","author":"Chang","year":"2023","journal-title":"Gondwana Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s11431-010-3219-x","article-title":"A New Method of Pseudo Absence Data Generation in Landslide Susceptibility Mapping with a Case Study of Shenzhen","volume":"53","author":"Xiao","year":"2010","journal-title":"Sci. China Technol. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.catena.2018.12.035","article-title":"Exploring the Effects of the Design and Quantity of Absence Data on the Performance of Random Forest-Based Landslide Susceptibility Mapping","volume":"176","author":"Hong","year":"2019","journal-title":"Catena"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Yuan, X., Liu, C., Nie, R., Yang, Z., Li, W., Dai, X., Cheng, J., Zhang, J., Ma, L., and Fu, X. (2022). A Comparative Analysis of Certainty Factor-Based Machine Learning Methods for Collapse and Landslide Susceptibility Mapping in Wenchuan County, China. Remote Sens., 14.","DOI":"10.3390\/rs14143259"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/s12665-021-09737-w","article-title":"Landslide Susceptibility Assessment for a Transmission Line in Gansu Province, China by Using a Hybrid Approach of Fractal Theory, Information Value, and Random Forest Models","volume":"80","author":"Zhao","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xu, C., Zhang, W., Yi, Y., and Xu, Q. (2019, January 31). Landslide Susceptibility Mapping Using Logistic Regression Model Based on Information Value for the Region Along China-Thailand Railway from Saraburi to Sikhio, Thailand. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8900041"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"589630","DOI":"10.3389\/feart.2021.589630","article-title":"Slope Unit-Based Landslide Susceptibility Mapping Using Certainty Factor, Support Vector Machine, Random Forest, CF-SVM and CF-RF Models","volume":"9","author":"Zhao","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ji, J., Zhou, Y., Cheng, Q., Jiang, S., and Liu, S. (2023). Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization. Land, 12.","DOI":"10.3390\/land12061125"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, Y., Deng, X., Ji, P., Yang, Y., Jiang, W., and Zhao, Z. (2022). Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture. Int. J. Environ. Res. Public. Health, 19.","DOI":"10.3390\/ijerph192114248"},{"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":"592","article-title":"An Evaluation of Two-Step Techniques for Positive-Unlabeled Learning in Text Classification","volume":"3","author":"Kaboutari","year":"2014","journal-title":"Int. J. Comput. Appl. Technol. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2919","DOI":"10.1007\/s10346-020-01473-9","article-title":"Landslide Susceptibility Prediction Based on a Semi-Supervised Multiple-Layer Perceptron Model","volume":"17","author":"Huang","year":"2020","journal-title":"Landslides"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s10064-022-02615-0","article-title":"Application of a Two-Step Sampling Strategy Based on Deep Neural Network for Landslide Susceptibility Mapping","volume":"81","author":"Yao","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Chang, Z., Du, Z., Zhang, F., Huang, F., Chen, J., Li, W., and Guo, Z. (2020). Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sens., 12.","DOI":"10.3390\/rs12030502"},{"key":"ref_42","first-page":"49","article-title":"Main structural characteristics of Yanshanian in Shengzhou area of Yuyao-Lishui fault zone (in Chinese)","volume":"5","author":"Zhu","year":"2018","journal-title":"Chin. Geol. Surv."},{"key":"ref_43","unstructured":"Chen, L.F. (2010). Study on the Activity of NE Trending Faults along the Coast of Zhejiang Province (in Chinese). [Master\u2019s Thesis, Zhejiang University]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1007\/s10346-022-01891-x","article-title":"The Fault-Controlled Chengtian Landslide Triggered by Rainfall on 20 May 2021 in Songyang County, Zhejiang Province, China","volume":"19","author":"Wang","year":"2022","journal-title":"Landslides"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1023\/B:NHAZ.0000007282.62071.75","article-title":"Is Prediction of Future Landslides Possible with a GIS?","volume":"30","author":"Fabbri","year":"2003","journal-title":"Nat. Hazards"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104851","DOI":"10.1016\/j.catena.2020.104851","article-title":"Landslide Susceptibility Mapping Using Multiscale Sampling Strategy and Convolutional Neural Network: A Case Study in Jiuzhaigou Region","volume":"195","author":"Yi","year":"2020","journal-title":"Catena"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1007\/s10064-022-02664-5","article-title":"Effectiveness of Newmark-Based Sampling Strategy for Coseismic Landslide Susceptibility Mapping Using Deep Learning, Support Vector Machine, and Logistic Regression","volume":"81","author":"Xi","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s10064-020-01863-2","article-title":"A Novel Landslide Susceptibility Mapping Portrayed by OA-HD and K-Medoids Clustering Algorithms","volume":"80","author":"Hu","year":"2021","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1007\/s10346-020-01558-5","article-title":"Spatial Clustering and Modelling for Landslide Susceptibility Mapping in the North of the Kathmandu Valley, Nepal","volume":"18","author":"Pokharel","year":"2021","journal-title":"Landslides"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.scitotenv.2019.01.221","article-title":"Assessment of Advanced Random Forest and Decision Tree Algorithms for Modeling Rainfall-Induced Landslide Susceptibility in the Izu-Oshima Volcanic Island, Japan","volume":"662","author":"Dou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10346-019-01286-5","article-title":"Improved Landslide Assessment Using Support Vector Machine with Bagging, Boosting, and Stacking Ensemble Machine Learning Framework in a Mountainous Watershed, Japan","volume":"17","author":"Dou","year":"2020","journal-title":"Landslides"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"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_54","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/s12665-015-4866-9","article-title":"Spatial Prediction of Landslide Hazard at the Luxi Area (China) Using Support Vector Machines","volume":"75","author":"Hong","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_55","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_56","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.gsf.2020.02.012","article-title":"Landslide Identification Using Machine Learning","volume":"12","author":"Wang","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.1021\/ct200463m","article-title":"MSMBuilder2: Modeling Conformational Dynamics on the Picosecond to Millisecond Scale","volume":"7","author":"Beauchamp","year":"2011","journal-title":"J. Chem. Theory Comput."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:03:44Z","timestamp":1760126624000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":57,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133345"],"URL":"https:\/\/doi.org\/10.3390\/rs15133345","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,30]]}}}