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Evaluating landslide susceptibility: an AHP method-based approach enhanced with optimized random forest modeling

Author

Listed:
  • Xuedong Zhang

    (Beijing University of Civil Engineering and Architecture
    Beijing Key Laboratory of Urban Spatial Information Engineering)

  • Haoyun Xie

    (Beijing University of Civil Engineering and Architecture
    Beijing Key Laboratory of Urban Spatial Information Engineering)

  • Zidong Xu

    (Beijing University of Civil Engineering and Architecture
    Beijing Key Laboratory of Urban Spatial Information Engineering)

  • Zhaowen Li

    (Beijing University of Civil Engineering and Architecture
    Beijing Key Laboratory of Urban Spatial Information Engineering)

  • Bo Chen

    (Beijing University of Civil Engineering and Architecture
    Beijing Key Laboratory of Urban Spatial Information Engineering)

Abstract

Understanding the extent of landslide damage is important for reducing the impact of landslides, which can cause great losses of life and property. Although numerous studies have been done on landslide disaster susceptibility, they have been limited by an unreasonable negative sample selection strategy or the absence of subjective environmental information of the study area in a single machine learning evaluation model. To evaluate landslide susceptibility based on sample optimization, we propose an analytic hierarchy process (AHP) method weighted by an improved random forest (RF) model. Based on the density analysis of landslide data, this method employs the certainty factor (CF) method to generate negative sample data. Correspondingly, ADB_RF, an enhanced RF model based on adaptive boosting (AdaBoost) is proposed to obtain objective weights, which are then combined with subjective weights obtained by the AHP (CF-combination). Additionally, a case study on the evaluation of landslide disasters was conducted in the Chuxiong Autonomous Prefecture of Yunnan, China. The results show the following: (1) the proposed landslide susceptibility evaluation method could objectively reflect the area prone to landslides with a high degree of accuracy and efficacy. (2) The area under the curve (AUC) of the CF-combination model reached 96.1%, indicating a high degree of accuracy. (3) In the northwestern region of Chuxiong Prefecture, more extremely high-risk areas were found than in the southeast; therefore, it has a high likelihood of experiencing another landslide disaster, which requires special attention. Accordingly, the research findings have significant reference value for preventing disasters and mitigating losses.

Suggested Citation

  • Xuedong Zhang & Haoyun Xie & Zidong Xu & Zhaowen Li & Bo Chen, 2024. "Evaluating landslide susceptibility: an AHP method-based approach enhanced with optimized random forest modeling," 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(9), pages 8153-8207, July.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:9:d:10.1007_s11069-023-06306-1
    DOI: 10.1007/s11069-023-06306-1
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    References listed on IDEAS

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    1. Paúl Carrión-Mero & Néstor Montalván-Burbano & Fernando Morante-Carballo & Adolfo Quesada-Román & Boris Apolo-Masache, 2021. "Worldwide Research Trends in Landslide Science," IJERPH, MDPI, vol. 18(18), pages 1-24, September.
    2. Ruichong Zhang & Shiwei Wu & Chengyu Xie & Qingfa Chen, 2022. "Risk Monitoring Level of Stope Slopes and Landslides in High-Altitude and Cold Mines," Sustainability, MDPI, vol. 14(13), pages 1-12, June.
    3. Kai Ke & Yichen Zhang & Jiquan Zhang & Yanan Chen & Chenyang Wu & Zuoquan Nie & Junnan Wu, 2023. "Risk Assessment of Earthquake–Landslide Hazard Chain Based on CF-SVM and Newmark Model—Using Changbai Mountain as an Example," Land, MDPI, vol. 12(3), pages 1-20, March.
    4. 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.
    5. Yanrong Liu & Zhongqiu Meng & Lei Zhu & Di Hu & Handong He, 2023. "Optimizing the Sample Selection of Machine Learning Models for Landslide Susceptibility Prediction Using Information Value Models in the Dabie Mountain Area of Anhui, China," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
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