Susceptibility assessment of debris flow in Gansu Province based on LA-GraphCAN

ObjectiveIn the current studies related to the susceptibility of debris flow disasters, the geographical location relationship and spatial dependence of debris flow disasters have hitherto not been taken into account. MethodsIn response to this problem, this article presents a debris flow susceptibility assessment approach based on LA-GraphCAN (local augmentation graph convolutional and attention network). Firstly, a debris flow dataset for Gansu Province was constructed, encompassing 4286 positive sample points and 5912 negative sample points. Based on the projected coordinates of the longitude and latitude of the sample points, a nearest neighbor graph was constructed using KNN to capture the intricate geographical location relationships among debris flow disaster points. Secondly, GCN was employed to effectively aggregate local neighborhood information, extract key geographical and environmental features, and deeply explore the interrelationships of the spatial structures between adjacent grids, thereby enabling the model to more precisely identify and comprehend the local spatial characteristics within the samples. Simultaneously, GAT was introduced to incorporate a dynamic attention mechanism and refine the feature representations. Finally, the validity of the proposed method was verified and compared and analyzed from different perspectives. ResultsThe results demonstrate that the area under the ROC curve, the accuracy rate, the precision rate, the recall rate, and the F1 score of the LA-GraphCAN model, which considers the geographical location relationship of debris flow disasters, are 0.9868, 0.9458, 0.9436, 0.9228, and 0.9331, respectively, outperforming models such as CNN and Decision tree. ConclusionBoth the performance evaluations and the assessment results of debris flow susceptibility in Gansu Province indicate that the LA-GraphCAN method, which takes into account the spatial dependence of debris flow disasters, yields superior assessment results and exhibits excellent applicability.