{"id":3220,"date":"2026-04-10T11:31:29","date_gmt":"2026-04-10T03:31:29","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=3220"},"modified":"2026-06-10T23:23:26","modified_gmt":"2026-06-10T15:23:26","slug":"landslide-hazard-assessment-based-on-improved-stacking-model","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=landslide-hazard-assessment-based-on-improved-stacking-model","title":{"rendered":"Landslide hazard assessment based on improved Stacking model"},"content":{"rendered":"\n
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<\/i> 2024<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 27, Issue 5<\/a><\/p>\n<\/div>\n\n\n\n

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Rongchang Guo1<\/sup><\/i><\/a>, Lingyan Yu1<\/sup>, Rui Zhang1<\/sup>, Chao Yuan1<\/sup>, and Pan He2<\/sup><\/p>\n\n\n\n

1<\/sup>School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China<\/p>\n\n\n\n

2<\/sup>Baoji Electric Service Section of Xi\u2019an Railway Bureau Xi\u2019an 710054, China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0April 17, 2023
Accepted:\u00a0July 12, 2023
Publication Date:\u00a0April 10, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Graph of landslide risk assessment factors<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202405_27(5).0002<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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The early warning of landslides is crucial in mitigating the losses caused by frequent and abrupt landslide disasters along the railway. The scientific construction of an evaluation model is pivotal in conducting a comprehensive landslide hazard assessment. Using a railway section in Ya\u2019an City as a case study, an improved Stacking model was developed to assess landslide hazard by selecting eight evaluation factors and employing support vector machines, random forests, K-neighborhood, and naive Bayesian learning. Logical regression was utilized as a meta learning tool to evaluate the model\u2019s performance. To address the issue of a limited number of input samples for the meta learner, the proposed approach incorporates reduced dimensionality data from the original dataset as input for the meta learner. This is based on the output of the base learner, resulting in the establishment of an improved Stacking model. The ROC curve is used to verify the accuracy of the model, compare the accuracy of the Stacking model and the single model before and after the improvement, and generate the risk zoning map of the study area. The results show that the AUCs of support vector machines, random forests, and stacking models are 0.8068, 0.8203, and 0.8368 , respectively, with good performance, while the accuracy of the improved stacking model reaches 0.8806 . A reference for the prevention and management of geological catastrophes, the accuracy of the landslide hazard zoning map created using ArcGIS in the research area has reached 0.853 , which is essentially compatible with the real distribution.<\/p>\n\n\n\n

Keywords:\u00a0Landslide; Support vector machines; Random forest; Stacking model<\/em><\/p>\n\n\n\n

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