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Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)

Author

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  • Yue Wang

    (The Key Laboratory of GIS Application Research, Chongqing Normal University, Chongqing 401331, China)

  • Deliang Sun

    (The Key Laboratory of GIS Application Research, Chongqing Normal University, Chongqing 401331, China)

  • Haijia Wen

    (Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, Chongqing 400045, China
    National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas, Chongqing 400044, China
    School of Civil Engineering, Chongqing University, Chongqing 400045, China)

  • Hong Zhang

    (The Key Laboratory of GIS Application Research, Chongqing Normal University, Chongqing 401331, China)

  • Fengtai Zhang

    (School of Management, Chongqing University of Technology, Chongqing 400054, China)

Abstract

To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. A landslide inventory was built by historical records; satellite images; and extensive field surveys. Subsequently; a geospatial database was established based on 987 historical landslides in the study area. Then; all the landslides were randomly divided into two datasets: 70% of them were used as the training dataset and 30% as the test dataset. Furthermore; under five primary conditioning factors (i.e., topography factors; geological factors; environmental factors; human engineering activities; and triggering factors), 22 secondary conditioning factors were selected to form an evaluation factor library for analyzing the landslide susceptibility. On this basis; the RF model training and the FR model mathematical analysis were performed; and the established models were used for the landslide susceptibility simulation in the entire area of Yunyang County. Next; based on the analysis results; the susceptibility maps were divided into five classes: very low; low; medium; high; and very high. In addition; the importance of conditioning factors was ranked and the influence of landslides was explored by using the RF model. The area under the curve (AUC) value of receiver operating characteristic (ROC) curve; precision; accuracy; and recall ratio were used to analyze the predictive ability of the above two LSM models. The results indicated a difference in the performances between the two models. The RF model (AUC = 0.988) performed better than the FR model (AUC = 0.716). Moreover; compared with the FR model; the RF model showed a higher coincidence degree between the areas in the high and the very low susceptibility classes; on the one hand; and the geographical spatial distribution of historical landslides; on the other hand. Therefore; it was concluded that the RF model was more suitable for landslide susceptibility evaluation in Yunyang County; because of its significant model performance; reliability; and stability. The outcome also provided a theoretical basis for application of machine learning techniques (e.g., RF) in landslide prevention; mitigation; and urban planning; so as to deliver an adequate response to the increasing demand for effective and low-cost tools in landslide susceptibility assessments.

Suggested Citation

  • Yue Wang & Deliang Sun & Haijia Wen & Hong Zhang & Fengtai Zhang, 2020. "Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China)," IJERPH, MDPI, vol. 17(12), pages 1-39, June.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:12:p:4206-:d:370785
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    References listed on IDEAS

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    3. 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.
    4. Jiakai Lu & Chao Ren & Weiting Yue & Ying Zhou & Xiaoqin Xue & Yuanyuan Liu & Cong Ding, 2023. "Investigation of Landslide Susceptibility Decision Mechanisms in Different Ensemble-Based Machine Learning Models with Various Types of Factor Data," Sustainability, MDPI, vol. 15(18), pages 1-49, September.
    5. Xiaoyi Wu & Yuanbao Song & Wei Chen & Guichuan Kang & Rui Qu & Zhifei Wang & Jiaxian Wang & Pengyi Lv & Han Chen, 2023. "Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm," Sustainability, MDPI, vol. 15(5), pages 1-17, February.
    6. Bo Cao & Qingyi Li & Yuhang Zhu, 2022. "Comparison of Effects between Different Weight Calculation Methods for Improving Regional Landslide Susceptibility—A Case Study from Xingshan County of China," Sustainability, MDPI, vol. 14(17), pages 1-15, September.
    7. Mustafa Kamal & Baolei Zhang & Jianfei Cao & Xin Zhang & Jun Chang, 2022. "Comparative Study of Artificial Neural Network and Random Forest Model for Susceptibility Assessment of Landslides Induced by Earthquake in the Western Sichuan Plateau, China," Sustainability, MDPI, vol. 14(21), pages 1-14, October.

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