Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks.

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.

Model evaluation of total phosphorus prediction based on model accuracy and interpretability for the surface water in the river network of the Jiangnan Plain, China.

Due to climatic and hydrological changes and human activities, eutrophication and frequent outbreaks of cyanobacteria are prominent in the Jiangnan Plain basin of China. Therefore, building a suitable model to accurately predict the phosphorus concentration in surface water is of practical significance to prevent the above problems. This study built 10 models to predict the phosphorus element in the surface water of the river network in the Jiangnan Plain. The main water types in the basin include the Yangtze River, the Beijing-Hangzhou Canal, and the Gehu Lake. The 10 models in different datasets have been comprehensively evaluated by the prediction accuracy and interpretability of the model, and the calculation of the partial dependence diagram (PDP) and SHAP has proved that there is a transparent response relationship between phosphorus and different factors. The results show that the Yangtze River, Beijing-Hangzhou Canal, and Gehu Lake are suitable for random forest, linear regression, and random forest models, respectively, under the comprehensive evaluation of the prediction accuracy and interpretability of the model. Models with low prediction accuracy often show strong interpretability. In different water body types, turbidity, water temperature, and chlorophyll-a are the three factors that affect the model in predicting phosphorus.