关键词: 汶川地震灾区, 泥石流流域识别, 集水面积阈值, SOM, CNN-KAN

Abstract:  The identification of potential debris flow basins often faces challenges such as unscientific watershed division criteria， unreasonable selection of non-debris flow basins, and insufficient model accuracy. A method combining river network density with the self-organizing map（SOM）is proposed to accurately determine the optimal catchment area threshold for watershed division， with the SOM used to generate representative non-debris flow basins. A CNN-KAN model， based on an improved traditional CNN architecture, is constructed to enhance identification accuracy. Experimental results indicate that the CNN-KAN model achieves a recognition accuracy of 92.9%， outperforming multilayer perceptron， KAN， and CNN models in precision， recall, F1 score， and AUC. The identified potential debris flow basins can serve as essential computational units and focal areas for debris flow early warning in the region. 

Key words:  , Wenchuan earthquake-affected area； debris flow basin identification； catchment area threshold； SOM； CNN-KAN