Bagging-based machine learning algorithms for landslide susceptibility modeling

Tingyu Zhang, Quan Fu, Hao Wang, Fangfang Liu, Huanyuan Wang () and Ling Han
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Tingyu Zhang: The Ministry of Natural Resources
Quan Fu: Shaanxi Provincial Land Engineering Construction Group Land Survey Planning and Design Institute Co., Ltd.
Hao Wang: Hanzhong Branch of Shaanxi Land Engineering Construction Group Co., Ltd.
Fangfang Liu: Shaanxi Provincial Land Engineering Construction Group Land Survey Planning and Design Institute Co., Ltd.
Huanyuan Wang: The Ministry of Natural Resources
Ling Han: Chang’an University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 110, issue 2, No 1, 823-846

Abstract: Abstract Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree, support vector machine and classification regression tree (CART) and were integrated with bagging strategy. Then, these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. Among them the Bag-CART model has the highest AUC value of 0.874; however, the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County.

Keywords: Landslide susceptibility; Bagging; Best-first decision tree; Functional tree; Classification and regression tree; Support vector machine (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-04986-1

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