Due to the limitations of poor data completeness and low knowledge utilization,it remains a challenge to accurately integrate multiple sources of heterogeneous disaster information to comprehensively evaluate the susceptibility of landslides.Therefore,a mixed framework for mapping the susceptibility of landslides with regional statistical constraints is proposed.Firstly,environmental factors such as topography,geomorphology,geological structure,meteorology and hydrology,and human activities were selected based on expert knowledge,and the relevant data is pre-processed such as cleaning and normalization.Then,from the perspective of"mechanism-geography-physics-mathematics",the feature mapping of disaster data is constructed,and a balanced sampling of positive and negative samples is carried out for historical disaster sites and non-disaster sites.On this basis,the administrative units are adaptively aggregated using a multi-scale spatial unit division method,and the study area is divided into a series of homogeneous sub-regions according to slope and average annual rainfall.Finally,under the constraint of regional statistics,a random forest model is constructed for sample training and susceptibility mapping.The experiments show that the proposed hybrid framework improves the accuracy of landslide susceptibility assessment by at least 9％.
维普
万方数据