A conditioning factor selection framework considering sample heterogeneity in debris flow susceptibility mapping

Debris flow susceptibility mapping (DFSM) is critical for disaster prevention, while challenges still exist in addressing selecting conditioning factors. This study aims to propose a novel framework for debris flow conditioning factor selection considering the sample heterogeneity problem. Utilizing the fuzzy C-means clustering technique, the study area was segmented into multiple homogeneous subareas. The predictive capacity of the conditioning factors was assessed by applying the information gain ratio approach. This evaluation was conducted on both the total dataset prior to clustering and the homogeneous datasets derived from the clustering procedure. Then random forest modeling was implemented on all the datasets following the elimination of the two conditioning factors exhibiting the weakest predictive ability. The prediction results of models built on homogeneous datasets need to be merged for evaluation. The total dataset and homogeneous datasets with all conditioning factors retained were also involved in model training for comparison. The results showed that reasonable conditioning factor selection could significantly improve the model performance. In addition, the conditioning factor selection framework proposed in this study that considers sample heterogeneity could provide better conditioning factor combinations for DFSM.