Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization

Junjie Ji, Yongzhang Zhou (), Qiuming Cheng, Shoujun Jiang and Shiting Liu
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Junjie Ji: School of Earth Science and Engineering, Sun Yat-sen University, Zhuhai 519000, China
Yongzhang Zhou: School of Earth Science and Engineering, Sun Yat-sen University, Zhuhai 519000, China
Qiuming Cheng: School of Earth Science and Engineering, Sun Yat-sen University, Zhuhai 519000, China
Shoujun Jiang: Guangdong Geological Survey Institute, Guangzhou 510275, China
Shiting Liu: The Sixth Geological Team of Guangdong Geological Bureau, Jiangmen 529000, China

Land, 2023, vol. 12, issue 6, 1-22

Abstract: Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative samples used for machine learning. Recurrent neural network (RNN) has a memory function, so when using an RNN for landslide susceptibility mapping purposes, the input order of the landslide-influencing factors affects the resulting quality of the model. The information value analysis calculates the landslide-influencing factors, determines the input order of data based on the importance of any specific factor in determining the landslide susceptibility, and improves the prediction potential of recurrent neural networks. The simple recurrent unit (SRU), a newly proposed variant of the recurrent neural network, is characterized by possessing a faster processing speed and currently has less application history in landslide susceptibility mapping. This study used recurrent neural networks optimized by information value analysis for landslide susceptibility mapping in Xinhui District, Jiangmen City, Guangdong Province, China. Four models were constructed: the RNN model with optimized negative sample selection, the SRU model with optimized negative sample selection, the RNN model, and the SRU model. The results show that the RNN model with optimized negative sample selection has the best performance in terms of AUC value (0.9280), followed by the SRU model with optimized negative sample selection (0.9057), the RNN model (0.7277), and the SRU model (0.6355). In addition, several objective measures of accuracy (0.8598), recall (0.8302), F1 score (0.8544), Matthews correlation coefficient (0.7206), and the receiver operating characteristic also show that the RNN model performs the best. Therefore, the information value analysis can be used to optimize negative sample selection in landslide sensitivity mapping in order to improve the model’s performance; second, SRU is a weaker method than RNN in terms of model performance.

Keywords: landslide susceptibility mapping; information value analysis; recurrent neural network; simple recurrent unit (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:6:p:1125-:d:1155164

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