Comparative analysis of the TabNet algorithm and traditional machine learning algorithms for landslide susceptibility assessment in the Wanzhou Region of China

Song Yingze (), Song Yingxu (), Zhang Xin (), Zhou Jie () and Yang Degang ()
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Song Yingze: Chongqing Normal University
Song Yingxu: East China University of Technology
Zhang Xin: Chongqing Normal University
Zhou Jie: Chongqing Normal University
Yang Degang: Chongqing Normal University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 8, No 26, 7627-7652

Abstract: Abstract Landslides, widespread and highly dangerous geological disasters, pose significant risks to humankind and the ecological environment. Consequently, predicting landslides is vital for disaster prevention and mitigation strategies. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. At present, the predominant methods for predicting landslide susceptibility are evolving from conventional machine learning techniques to deep learning approaches. Prior studies have shown that in the context of landslide susceptibility, these models frequently underperform relative to tree-based machine learning algorithms. This shortcoming has restricted the application of deep learning in this domain. To overcome this challenge, this study presents the TabNet algorithm, which combines the interpretability and selective feature extraction of tree models with the representation learning and comprehensive training capabilities of neural network models. This paper explores the potential of employing the TabNet algorithm for landslide susceptibility analysis in China’s WanZhou region and evaluates its performance against traditional machine learning techniques. The experimental data indicate that the TabNet algorithm achieves a recall score of 0.898 and an AUC of 0.915, demonstrating a generalization capability that is comparable to that of classical machine learning algorithms.

Keywords: Landslide susceptibility assessment; Tabnet; Deep learing; Neural network (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06521-4

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