Development of a region-partitioning method for debris flow susceptibility mapping

Debris flow susceptibility mapping(DFSM)has been reported in many studies,however,the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved.In this paper,a region-partitioning method that is based on the topographic characteristics of watershed units was developed with the objective of establishing multiple conditioning factor systems for regional-scale DFSM.First,watershed units were selected as the mapping units and created throughout the entire research area.Four topographical factors,namely,elevation,slope,aspect and relative height difference,were selected as the basis for clustering watershed units.The k-means clustering analysis was used to cluster the watershed units according to their topographic characteristics to partition the study area into several parts.Then,the information gain ratio method was used to filter out superfluous factors to establish conditioning factor systems in each region for the subsequent debris flow susceptibility modeling.Last,a debris flow susceptibility map of the whole study area was acquired by merging the maps from all parts.DFSM of Yongji County in Jilin Province,China was selected as a case study,and the analytical hierarchy process method was used to conduct a comparative analysis to evaluate the performance of the region-partitioning method.The area under curve(AUC)values showed that the partitioning of the study area into two parts improved the prediction rate from 0.812 to 0.916.The results demonstrate that the region-partitioning method on the basis of topographic characteristics of watershed units can realize more reasonable regional-scale DFSM.Hence,the developed region-partitioning method can be used as a guide for regional-scale DFSM to mitigate the imminent debris flow risk.

Debris Flow Susceptibility Evaluation in Meizoseismal Region:A Case Study in Jiuzhaigou,China

Jiuzhaigou is situated on a mountain-canyon region and is famous for frequent tectonic activities.An abundance of loose co-seismic landslides and collapses were produced on gullies after the Jiuzhaigou Earthquake on August 8,2017,which was served as material source for debris flow in later years.Debris flow appears frequently which are seriously endangering the safety of people's lives and properties.Even the earliest debris flow appeared in areas where no case ever reported before.The debris flow susceptibility evaluation(DFSE)is used for predicting the areas prone to debris flow,which is urgently required to avoid hazards and help to guide the strategy of preventive measures.Therefore,this work employs debris flow in Jiuzhaigou to reveal the characteristics of disaster-pregnant environment and to explore the application of machine learning in DFSE.Some new viewpoints are suggested:(i)Material density factor of debris flow is first adopted in this work,and it is proved to be a critical factor for triggering debris flows by sensitivity analysis method.(ii)Deep neural network and convolutional neural network(CNN)achieve relatively good area under the curve(AUC)values and are 0.021-0.024 higher than traditional machine learning methods.(iii)Watershed units combined with CNNbased model can achieve more accurate,reliable and practical susceptibility map.This work provides an idea for prevention of debris flow in mountainous lands.