Landslide susceptibility assessment based on the SOM-I-SVM model

When using machine learning models for landslide susceptibility evaluation, the non-landslide sample points are usually selected randomly outside the landslide influence area, leading to a certain error. To improve the accuracy of landslide susceptibility evaluation, this paper couples the self-organizing map (SOM) neural network, information (I) model, and support vector machine (SVM) model, and proposes a SOM-I-SVM model-based method of landslide susceptibility evaluation, comparing with K-means clustering to verify the reliability of this model. The Maojian District of the city of Shiyan is taken as an example, and seven factors of the distance from water system, slope, rainfall, distance from structure, relative height difference, distance from road, stratigraphic lithology are selected by correlation and importance analyses of environmental factors to establish a landslide susceptibility evaluation system. Based on these, the graded information values of each factor are calculated and used as input varia