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Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math

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  • Xianyu Yu

    (School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
    Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes, Hubei University of Technology, Wuhan 430068, China)

  • Yang Xia

    (School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China)

  • Jianguo Zhou

    (School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
    Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes, Hubei University of Technology, Wuhan 430068, China)

  • Weiwei Jiang

    (School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
    Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes, Hubei University of Technology, Wuhan 430068, China)

Abstract

Landslides pose a great threat to the safety of people’s lives and property within disaster areas. In this study, the Zigui to Badong section of the Three Gorges Reservoir is used as the study area, and the land use (LU), land use change (LUC) and band math (band) factors from 2016–2020 along with six selected commonly used factors are used to form a land use factor combination (LUFC), land use change factor combination (LUCFC) and band math factor combination (BMFC). An artificial neural network (ANN), a support vector machine (SVM) and a convolutional neural network (CNN) are chosen as the three models for landslide susceptibility mapping (LSM). The results show that the BMFC is generally better than the LUFC and the LUCFC. For the validation set, the highest simple ranking scores for the three models were obtained for the BMFC (37.2, 32.8 and 39.2), followed by the LUFC (28, 26.6 and 31.8) and the LUCFC (26.8, 28.6 and 20); that is, the band-based predictions are better than those based on the LU and LUC, and the CNN model provides the best prediction ability. According to the four groups of experimental results with ANNs, compared with LU and LUC, band is easier to access, yields higher predictive performance, and provides stronger stability. Thus, band can replace LU and LUC to a certain extent and provide support for automatic and real-time landslide monitoring.

Suggested Citation

  • Xianyu Yu & Yang Xia & Jianguo Zhou & Weiwei Jiang, 2023. "Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math," Sustainability, MDPI, vol. 15(3), pages 1-29, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:2226-:d:1046447
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    References listed on IDEAS

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    1. Xianyu Yu & Yi Wang & Ruiqing Niu & Youjian Hu, 2016. "A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, Chin," IJERPH, MDPI, vol. 13(5), pages 1-35, May.
    2. Abhik Saha & Vasanta Govind Kumar Villuri & Ashutosh Bhardwaj, 2022. "Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India," Land, MDPI, vol. 11(10), pages 1-27, October.
    3. Shuai Zhao & Zhou Zhao, 2021. "A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, January.
    4. Hamid Reza Pourghasemi & Nitheshnirmal Sadhasivam & Mahdis Amiri & Saeedeh Eskandari & M. Santosh, 2021. "Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques," 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. 108(1), pages 1291-1316, August.
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    Cited by:

    1. Deborah Simon Mwakapesa & Yimin Mao & Xiaoji Lan & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance," Sustainability, MDPI, vol. 15(5), pages 1-20, February.

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