期刊
REMOTE SENSING
卷 14, 期 14, 页码 -出版社
MDPI
DOI: 10.3390/rs14143408
关键词
landslide susceptibility mapping; toolbox; SVM; automatic; multiprocessing; the whole process
类别
资金
- National Natural Science Foundation of China [41941019, 42090053]
- National Key Research and Development Program of China [2021YFC3000400]
- Shaanxi Province Science and Technology Innovation team [2021TD-51]
- Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team (2022)
- Fundamental Research Funds for the Central Universities, CHD [300102260301, 300102261108, 300102262902, 300102269208, 300102260404]
- Fund Project of Shaanxi Key Laboratory of Land Consolidation [2019-ZD04]
The study developed a toolbox called SVM-LSM for landslide susceptibility mapping, which consists of three sub-toolboxes. Using support vector machine technology, the toolbox efficiently realizes the entire process required for LSM. Experimental results confirm the accuracy and efficiency of the toolbox in susceptibility prediction process.
Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the SVM-LSM toolbox. The toolbox realizes landslide susceptibility mapping based on a support vector machine (SVM), which can be integrated into the ArcGIS or ArcGIS Pro platform. The toolbox includes three sub-toolboxes, namely: (1) influence factor production, (2) factor selection and dataset production, and (3) model training and prediction. Influence factor production provides automatic calculation of DEM-related topographic factors, converts line vector data to continuous raster factors, and performs rainfall data processing. Factor selection uses the Pearson correlation coefficient (PCC) to calculate the correlations between factors, and the information gain ratio (IGR) to calculate the contributions of different factors to landslide occurrence. Dataset sample production includes the automatic generation of non-landslide data, data sample production and dataset split. The accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) are used to evaluate the prediction ability of the model. In addition, two methods-single processing and multiprocessing-are used to generate LSM. The prediction efficiency of multiprocessing is much higher than that of the single process. In order to verify the performance and accuracy of the toolbox, Wuqi County, Yan'an City, Shaanxi Province was selected as the test area to generate LSM. The results show that the AUC value of the model is 0.8107. At the same time, the multiprocessing prediction tool improves the efficiency of the susceptibility prediction process by about 60%. The experimental results confirm the accuracy and practicability of the proposed toolbox in LSM.
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