{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:36:51Z","timestamp":1775695011036,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan International Joint Laboratory of China\u2013Laos\u2013Bangladesh\u2013Myanmar Natural Resources Remote Sensing Monitoring","award":["202303AP140015"],"award-info":[{"award-number":["202303AP140015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To address the subjectivity of traditional factor attribute grading methods and the weak predictive capabilities of single-model classifications, this study focused on Yulong County; the Contribution Degree Clustering Method (CDCM) utilizes the Certainty Factor (CF) as the contribution index to partition continuous factor attribute intervals. Additionally, the Sparrow Search Optimization Algorithm (SSA) is employed for hyperparameter tuning. The CF is incorporated into Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Random Forest (RF) models to form the CF-SSA-SVM, CF-SSA-BPNN, and CF-SSA-RF coupling models, respectively. These basic coupling models are further integrated using the Stacking algorithm to create the CF-SSA-Stacking integrated coupling model for constructing a landslide susceptibility assessment system. The results indicate that the CF-SSA-Stacking integrated coupling model achieves the highest accuracy, F1 score, Kappa coefficient, and AUC value, with values of 0.89375, 0.89172, 0.787500, and 0.9522, respectively. These metrics are significantly superior to those of the three basic coupling models, demonstrating better generalization capability and reliability. This suggests that the model can identify more historical landslide occurrences using fewer grid areas classified as extremely-high- or high-susceptibility zones. It is suitable as an effective regional landslide susceptibility assessment method for practical disaster prevention and mitigation applications. Further studies could explore the model\u2019s performance across varying geological settings or with different datasets, providing a roadmap for future research and development in landslide susceptibility assessment.<\/jats:p>","DOI":"10.3390\/rs16193582","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T04:05:46Z","timestamp":1727323546000},"page":"3582","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Landslide Susceptibility Assessment in Yulong County Using Contribution Degree Clustering Method and Stacking Ensemble Coupled Model Based on Certainty Factor"],"prefix":"10.3390","volume":"16","author":[{"given":"Yang","family":"Qin","sequence":"first","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}]},{"given":"Zhifang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Yunnan University, Kunming 650500, China"},{"name":"Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring, Kunming 650051, China"},{"name":"Research Center of Domestic High-Resolution Satellite Remote Sensing Geological Engineering, Universities in Yunnan Province, Kunming 650500, China"},{"name":"Yunnan Key Laboratory of Sanjiang Metallogeny and Resources Exploration and Utilization, Kunming 650051, China"}]},{"given":"Dingyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0021-1164","authenticated-orcid":false,"given":"Kangtai","family":"Chang","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}]},{"given":"Qiaomu","family":"Mou","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}]},{"given":"Yonglin","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}]},{"given":"Yunfei","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14309","DOI":"10.1080\/10106049.2022.2087753","article-title":"A Bibliometric Analysis of the Landslide Susceptibility Research (1999\u20132021)","volume":"37","author":"Liu","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Miao, F., Ruan, Q., Wu, Y., Qian, Z., Kong, Z., and Qin, Z. (2023). Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model. Remote Sens., 15.","DOI":"10.3390\/rs15225427"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhou, D., Zhao, Z., Xi, W., Zhao, X., and Chao, J. (2024). New Method for Landslide Susceptibility Evaluation in Alpine Valley Regions That Considers the Suitability of InSAR Monitoring and Introduces Deformation Rate Grading. Geo-Spat. Inf. Sci., 1\u201324.","DOI":"10.1080\/10095020.2023.2270218"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1007\/s10064-015-0786-x","article-title":"GIS-Based Landslide Susceptibility Mapping with Logistic Regression, Analytical Hierarchy Process, and Combined Fuzzy and Support Vector Machine Methods: A Case Study from Wolong Giant Panda Natural Reserve, China","volume":"75","author":"Meng","year":"2016","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40677-020-00170-y","article-title":"Landslide Susceptibility Mapping Using Statistical Methods in Uatzau Catchment Area, Northwestern Ethiopia","volume":"8","author":"Wubalem","year":"2021","journal-title":"Geoenviron. Disasters"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1016\/j.asr.2022.01.043","article-title":"Spatiotemporal Assessment of Landslide Susceptibility in Southern Sichuan, China Using SA-DBN, PSO-DBN and SSA-DBN Models Compared with DBN Model","volume":"69","author":"Li","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_8","first-page":"281","article-title":"Improved Classification Algorithm for Stacking Integration","volume":"39","author":"Lu","year":"2022","journal-title":"Comput. Appl. Softw."},{"key":"ref_9","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_10","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_11","first-page":"1570","article-title":"Landslide Susceptibility Prediction Based on Non-Landslide Samples Selection and Heterogeneous Ensemble Machine Learning","volume":"25","author":"Zhou","year":"2023","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Xiao, B., Zhao, J., Li, D., Zhao, Z., Zhou, D., Xi, W., and Li, Y. (2022). Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China. Sensors, 22.","DOI":"10.3390\/s22208041"},{"key":"ref_14","first-page":"169","article-title":"Uncertainties of Landslide Susceptibility Prediction Modeling: Influence of Different Selection Methods of \u201cNon-Landslide Samples\u201d","volume":"56","author":"Huang","year":"2024","journal-title":"Adv. Eng. Sci."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1007\/s10064-023-03392-0","article-title":"Comparison of Natural Breaks Method and Frequency Ratio Dividing Attribute Intervals for Landslide Susceptibility Mapping","volume":"82","author":"Ke","year":"2023","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sannino, A., Amoruso, S., Boselli, A., Wang, X., and Zhao, Y. (2022). Aerosol Monitoring at High Mountains Remote Station: A Case Study on the Yunnan Plateau (China). Remote Sens., 14.","DOI":"10.3390\/rs14153773"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.5194\/essd-13-3907-2021","article-title":"The 30 m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019","volume":"13","author":"Yang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_19","first-page":"105","article-title":"Calculation Tool of Topographic Factors","volume":"13","author":"Fu","year":"2016","journal-title":"Sci. Soil. Water Conserv."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.enggeo.2017.01.016","article-title":"Landslide Displacement Prediction Based on Multivariate Chaotic Model and Extreme Learning Machine","volume":"218","author":"Huang","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7303","DOI":"10.1080\/10106049.2021.1973115","article-title":"Detection of Areas Prone to Flood-Induced Landslides Risk Using Certainty Factor and Its Hybridization with FAHP, XGBoost and Deep Learning Neural Network","volume":"37","author":"Costache","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xing, Y., Chen, Y., Huang, S., Xie, W., Wang, P., and Xiang, Y. (2023). Research on the Uncertainty of Landslide Susceptibility Prediction Using Various Data-Driven Models and Attribute Interval Division. Remote Sens., 15.","DOI":"10.3390\/rs15082149"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.geomorph.2007.01.008","article-title":"Mapping Landslide Susceptibility from Small Datasets: A Case Study in the Pays de Herve (E Belgium)","volume":"89","author":"Demoulin","year":"2007","journal-title":"Geomorphology"},{"key":"ref_25","first-page":"63","article-title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_26","first-page":"61","article-title":"A New Method of Pseudo Absence Data Generation in Landslide Susceptibility Mapping","volume":"32","author":"Miao","year":"2016","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105972","DOI":"10.1016\/j.enggeo.2020.105972","article-title":"Assessment of Landslide Susceptibility Mapping Based on Bayesian Hyperparameter Optimization: A Comparison between Logistic Regression and Random Forest","volume":"281","author":"Sun","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lu, Z., Yang, H., Zeng, W., Liu, P., and Wang, Y. (2023). Geological Hazard Identification and Susceptibility Assessment Based on MT-InSAR. Remote Sens., 15.","DOI":"10.3390\/rs15225316"},{"key":"ref_29","unstructured":"Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates. [2nd ed.]."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1080\/19475705.2012.662915","article-title":"A Comparative Assessment of Prediction Capabilities of Dempster-Shafer and Weights-of-Evidence Models in Landslide Susceptibility Mapping Using GIS","volume":"4","author":"Pourghasemi","year":"2013","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","article-title":"A Novel Swarm Intelligence Optimization Approach: Sparrow Search Algorithm","volume":"8","author":"Xue","year":"2020","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"101100","DOI":"10.1016\/j.gsf.2020.10.007","article-title":"Flash Flood Susceptibility Mapping Using a Novel Deep Learning Model Based on Deep Belief Network, Back Propagation and Genetic Algorithm","volume":"12","author":"Shahabi","year":"2021","journal-title":"Geosci. Front."},{"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":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked Generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s11069-020-04169-4","article-title":"Comparison of New Individual and Hybrid Machine Learning Algorithms for Modeling and Mapping Fire Hazard: A Supplementary Analysis of Fire Hazard in Different Counties of Golestan Province in Iran","volume":"104","author":"Eskandari","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2253","DOI":"10.1214\/19-AOS1886","article-title":"Minimax Optimal Rates for Mondrian Trees and Forests","volume":"48","author":"Mourtada","year":"2020","journal-title":"Ann. Stat."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.cageo.2012.08.023","article-title":"A Comparative Study on the Predictive Ability of the Decision Tree, Support Vector Machine and Neuro-Fuzzy Models in Landslide Susceptibility Mapping Using GIS","volume":"51","author":"Pradhan","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_39","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017, January 4\u20139). A Unified Approach to Interpreting Model Predictions. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"13419","DOI":"10.1080\/10106049.2022.2076928","article-title":"An Interpretable Model for the Susceptibility of Rainfall-Induced Shallow Landslides Based on SHAP and XGBoost","volume":"37","author":"Zhou","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Li, G., Tan, Z., Xu, W., Xu, F., Wang, L., Chen, J., and Wu, K. (2021). A Particle Swarm Optimization Improved BP Neural Network Intelligent Model for Electrocardiogram Classification. BMC Med. Inform. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01453-6"},{"key":"ref_42","first-page":"4535","article-title":"Uncertainties of Landslide Susceptibility Prediction: Different Attribute Interval Divisions of Environmental Factors and Different Data-Based Models","volume":"45","author":"Huang","year":"2021","journal-title":"Earth Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101317","DOI":"10.1016\/j.gsf.2021.101317","article-title":"Uncertainty Pattern in Landslide Susceptibility Prediction Modelling: Effects of Different Landslide Boundaries and Spatial Shape Expressions","volume":"13","author":"Huang","year":"2022","journal-title":"Geosci. Front."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/j.jrmge.2022.07.009","article-title":"Landslide Susceptibility Prediction Using Slope Unit-Based Machine Learning Models Considering the Heterogeneity of Conditioning Factors","volume":"15","author":"Chang","year":"2023","journal-title":"J. Rock. Mech. Geotech. Eng."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1022859003006","article-title":"Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy","volume":"51","author":"Kuncheva","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Niculescu-Mizil, A., and Caruana, R. (2005, January 7\u201311). Predicting Good Probabilities with Supervised Learning. Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.","DOI":"10.1145\/1102351.1102430"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A Systematic Analysis of Performance Measures for Classification Tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"115736","DOI":"10.1016\/j.eswa.2021.115736","article-title":"Explaining Anomalies Detected by Autoencoders Using Shapley Additive Explanations","volume":"186","author":"Antwarg","year":"2021","journal-title":"Expert. Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4601","DOI":"10.1007\/s11069-023-06374-3","article-title":"Application and Interpretability of Ensemble Learning for Landslide Susceptibility Mapping along the Three Gorges Reservoir Area, China","volume":"120","author":"Liu","year":"2024","journal-title":"Nat. Hazards"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0013-7952(01)00093-X","article-title":"Landslide Risk Assessment and Management: An Overview","volume":"64","author":"Dai","year":"2002","journal-title":"Eng. Geol."},{"key":"ref_52","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_53","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3582\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:03:32Z","timestamp":1760112212000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/19\/3582"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,26]]},"references-count":53,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["rs16193582"],"URL":"https:\/\/doi.org\/10.3390\/rs16193582","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,26]]}}}