A debris flow susceptibility mapping study considering sample heterogeneity

Susceptibility mapping has been an effective approach to manage the threat of debris flows. However, the sample heterogeneity problem has rarely been considered in previous studies. This paper is to explore the effect of sample heterogeneity on susceptibility mapping and propose corresponding solutions. Two unsupervised clustering approaches including K-means clustering and fuzzy C-means clustering were introduced to divide the study area into several homogeneous regions, each region was processed independently to solve the sample heterogeneity problem. The information gain ratio method was used to evaluate the predictive ability of the conditioning factors in the total dataset before clustering and the homogeneous datasets after clustering. Then the total dataset and the homogeneous datasets were involved in the random forest modeling. The receiver operating characteristic curves and related statistical results were employed to evaluate the model performance. The results showed that there was a significant sample heterogeneity problem for the study area, and the fuzzy C-means algorithm can play an important role in solving this problem. By dividing the study area into several homogeneous regions to process independently, conditioning factors with better predictive ability, models with better performance and debris flow susceptibility maps with higher quality could be obtained.