基于CNN-SHAP的小清河入海总氮通量影响因素分析OA北大核心

This study proposes an interpretable prediction model for total nitrogen flux from rivers to the sea to address the problem of total nitrogen pollution in China's coastal waters.The model is based on a Convolutional Neural Network(CNN)and the SHAP(SHapley Additive exPlanations)methods.It couples the river network to-pology structure simulated by a Markov chain and fully utilizes multisource spatiotemporal data.For the purposes of this study,the model is applied to the Xiaoqing River.Multisource data,such as meteorology,land use,soil type,and point and nonpoint source nitrogen emissions in the Xiaoqing River Basin,are converted into three-dimensional input data based on the Markov chain river network structure.Model evaluation shows that the model with three-dimensional input performs better in both the training set and the test set,achieving higher accuracy.The correlation coefficient of the predicted inflow flux reaches 0.99.The SHAP method is used to identify the key fac-tors that affect the model's prediction and analyze the influence of spatial features on the prediction,revealing dif-ferences in the impact of different spatial locations in the basin on the total nitrogen flux to the sea.The research results not only improve the accuracy of the prediction of sea water quality but also provide a scientific basis for the management of the coastal environment.