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• W3136247676 abstract "Global warming increases the frequency and intensity of extreme rainfall, putting many areas at risk of landslides. Landslide susceptibility assessment is essential to understand the threats and to predict, prevent, and mitigate landslides. In this study, a soil landslide inventory was constructed based on satellite images, topological maps, and extensive field studies. Subsequently, eight different GIS layers, which were geomorphology, elevation, slope angle, slope aspect, slope structure, slope curvature, antecedent rainfall, and cumulative rainfall on 16 September, were produced as control factors of soil planar slides for the susceptibility mapping. Landslide susceptibility mapping was performed using two different methods, logistic regression model and backpropagation (BP) neural network. Landslide susceptibility in the study area is divided into four levels, which are high, moderate, low, and no susceptibility in both the logistic regression model and the BP neural network model. In both the two models, most of the observed soil planar slides were located in areas with high or moderate susceptibility. For the logistic regression model, total 605 soil planar slides locate in the area with high susceptibility, of which the area is 800.56 km2, accounting for 40.31% of the total area. Finally, the validation of two models was evaluated. The AUC value of the logistic regression model was 0.878 and the parameters of BP neural network has the correlation coefficient of 0.880, which shows the two models are both reliable and reasonable for predicting the spatial susceptibility of soil planar slides. According to field checks, the BP neural network model is verified to have more accurate spatial prediction performance than the logistic regression model." @default.
• W3136247676 created "2021-03-29" @default.
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• W3136247676 date "2021-01-01" @default.
• W3136247676 modified "2023-10-17" @default.
• W3136247676 title "GIS-based soil planar slide susceptibility mapping using logistic regression and neural networks: a typical red mudstone area in southwest China" @default.
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• W3136247676 doi "https://doi.org/10.1080/19475705.2021.1896584" @default.
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