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Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division: Case Study of Fengjie County in Chongqing
Zhang Wengang, He Yuwei, Wang Luqi, Liu Songlin, Chen Bolin
PDF(8343 KB)
PDF(8343 KB)
Machine Learning Solution for Landslide Susceptibility Based on Hydrographic Division: Case Study of Fengjie County in Chongqing
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The Three Gorges Reservoir Area is the key area for geological disaster management, and the hydraulic effect of the Yangtze River on the slopes along its banks cannot be ignored. Therefore, it is necessary to study the influence of drainage factors on landslide susceptibility. The historical landslides points in Fengjie County and their corresponding features are taken as analysis data. Due to the significant influence of regional water system, the research area is divided into two sub-zones according to hydrographic conditions. Area of 300 meters along the two sides of the rivers is regarded as Sub-Zone I, and the remaining area is defined as Sub-Zone II.Then, a total 16 influencing factors are selected to establish landslide susceptibility evaluating models, and the landslide susceptibility evaluation results of the whole region and sub-zones were compared and analyzed. The following results of landslide susceptibility analysis based on machine learningalgorithm can be obtained. Because the fluctuation of reservoir water level reduces the effective stress of anti-slip section and the cultivated land has weak conservation effect on slope mass owing to the destruction of the original mountain balance in the process of reclamation, the areas with high and extremely high probability of landslide occurrences in Fengjie County mainly lie on the bank of rivers and in the area of cultivated land. The accuracy of susceptibility assessment of the hydrographic-divided model is better than the whole-range model. Specifically, the accuracy and F-score are improved by 5.1% and 5.2%, which indicates the practicability and validity of conducting zone-dividing susceptibility analysis.
landslide / machine learning / sub-zone based on hydrographic division / random forest / susceptibility assessment / engineering geology
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