Selective ensemble deep bidirectional RVFLN for landslide displacement prediction

Xiaoyang Yu, Cheng Lian (), Yixin Su, Bingrong Xu, Xiaoping Wang, Wei Yao and Huiming Tang
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Xiaoyang Yu: Wuhan University of Technology
Cheng Lian: Wuhan University of Technology
Yixin Su: Wuhan University of Technology
Bingrong Xu: Huazhong University of Science and Technology
Xiaoping Wang: Huazhong University of Science and Technology
Wei Yao: South-Central University for Nationalities
Huiming Tang: China University of Geosciences

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 112, issue 1, No 31, 725-745

Abstract: Abstract Landslide displacement prediction is a challenging and important subject in landslide research. To improve the prediction accuracy of and reduce disasters caused by landslides, we propose a selective ensemble deep bidirectional Random Vector Functional Link Network (sedb-RVFLN) for landslide displacement prediction in which each independent hidden layer is linked to a different output layer. In this paper, to reduce the number of hidden nodes without affecting the efficiency of network training, an incremental learning method is utilized to make some hidden nodes not randomly chosen. Moreover, we apply selected partial hidden layers instead of all hidden layers to construct a selective ensemble. The ensemble method adopted by sedb-RVFLN does not require training multiple independent networks, and the entire sedb-RVFLN only needs to be trained once. Finally, we conduct extensive experiments on real landslide datasets from the Huangdeng Hydropower Station in China to demonstrate the effectiveness of our model.

Keywords: Landslide displacement prediction; Random Vector Functional Link Network; Incremental learning; Selective ensemble (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05202-w

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