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

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  • Junjie Ji

    (School of Earth Science and Engineering, Sun Yat-sen University, Zhuhai 519000, China
    Guangdong Provincial Key Laboratory of Geological Processes and Mineral Resource Survey, Guangzhou 510275, China
    School of Earth Sciences and Engineering, Center for Earth Environment & Resources, Sun Yat-sen University, Guangzhou 510275, China)

  • Yongzhang Zhou

    (School of Earth Science and Engineering, Sun Yat-sen University, Zhuhai 519000, China
    Guangdong Provincial Key Laboratory of Geological Processes and Mineral Resource Survey, Guangzhou 510275, China
    School of Earth Sciences and Engineering, Center for Earth Environment & Resources, Sun Yat-sen University, Guangzhou 510275, 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)

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.

Suggested Citation

  • Junjie Ji & Yongzhang Zhou & Qiuming Cheng & Shoujun Jiang & Shiting Liu, 2023. "Landslide Susceptibility Mapping Based on Deep Learning Algorithms Using Information Value Analysis Optimization," Land, MDPI, vol. 12(6), pages 1-22, May.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:6:p:1125-:d:1155164
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    References listed on IDEAS

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    1. Yumiao Wang & Xueling Wu & Zhangjian Chen & Fu Ren & Luwei Feng & Qingyun Du, 2019. "Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China," IJERPH, MDPI, vol. 16(3), pages 1-27, January.
    2. Freedman, Seth & Jin, Ginger Zhe, 2017. "The information value of online social networks: Lessons from peer-to-peer lending," International Journal of Industrial Organization, Elsevier, vol. 51(C), pages 185-222.
    3. Frank Press, 2008. "Earth science and society," Nature, Nature, vol. 451(7176), pages 301-303, January.
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    Cited by:

    1. Shaohan Zhang & Shucheng Tan & Yongqi Sun & Duanyu Ding & Wei Yang, 2024. "Risk Mapping of Geological Hazards in Plateau Mountainous Areas Based on Multisource Remote Sensing Data Extraction and Machine Learning (Fuyuan, China)," Land, MDPI, vol. 13(9), pages 1-25, August.
    2. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11485-11525, October.

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