Identification of precipitation trend and landslide susceptibility analysis in Miandoab County using MATLAB

A python system for regional landslide susceptibility assessment by integrating machine learning models and its application

Landslide susceptibility assessment is considered the first step in landslide risk assessment, but current studies mostly rely on GIS platforms or other software for data preprocessing. The modeling process is relatively complicated and multi-models cannot be integrated. With regard to this issue, this study develops a Python system for automatic assessment of regional landslide susceptibility. The Python system implements landslide susceptibility assessment through three modules: geographic data processing, machine learning modeling and result evaluation analysis. For geographic data processing, ten landslide influencing factors can be used to construct an evaluation factor dataset and reclassify the thematic maps based on the frequency ratio method. Four built-in machine learning models (logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM) and extreme gradient boosting (XGBoost)) are integrated into the system to complete susceptibility modeling and calculation. Additionally, receiver operating characteristic (ROC) curves can be automatically generated to evaluate the accuracy. The system was then applied into Lantian County in Shaanxi Province as a demonstration example. The results show that the areas under the ROC curve (AUC) of the four models are 0.838 (LR)、0.882 (SVM)、0.809 (MLP) and 0.812 (XGBoost), respectively, indicating that the SVM model was the most suitable model for landslide susceptibility assessment in Lantian County in the Loess Plateau of China. The system has now been made open source on Github, which can effectively improve the efficiency of regional landslide susceptibility assessment, especially provide tools for data processing and modeling for non-professionals.

Dynamic landslide susceptibility analysis that combines rainfall period, accumulated rainfall, and geospatial information

Worldwide, catastrophic landslides are occurring as a result of abnormal climatic conditions. Since a landslide is caused by a combination of the triggers of rainfall and the vulnerability of spatial information, a study that can suggest a method to analyze the complex relationship between the two factors is required. In this study, the relationship between complex factors (rainfall period, accumulated rainfall, and spatial information characteristics) was designed as a system dynamics model as variables to check the possibility of occurrence of vulnerable areas according to the rainfall characteristics that change in real-time. In contrast to the current way of predicting the collapse time by analysing rainfall data, the developed model can set the precipitation period during rainfall. By setting the induced rainfall period, the researcher can then assess the susceptibility of the landslide-vulnerable area. Further, because the geospatial information features and rainfall data for the 672 h before the landslide's occurrence were combined, the results of the susceptibility analysis could be determined for each topographical characteristic according to the rainfall period and cumulative rainfall change. Third, by adjusting the General cumulative rainfall period (D_G) and Inter-event time definition (IETD), the preceding rainfall period can be adjusted, and desired results can be obtained. An analysis method that can solve complex relationships can contribute to the prediction of landslide warning times and expected occurrence locations.