Research on Water Resource Carrying Capacity Assessment and Water Quality Forecasting Based on Feature Selection with CNN-BiLSTM-Attention Model of the Min River Basin

To achieve a more accurate assessment of water resource carrying capacity (WRCC), the indicators of water resources, social resources, and ecological environment were selected to construct the WRCC system on the basis of the combinatorial assignment method with advantages. Moreover, the incorporation of key water quality influences into water quality predictions facilitated the performance of predictive models. Adaptive Lasso Regression was used to select key factors affecting water quality, whereas the CatBoost algorithm ranked the importance of the key factors selected by Adaptive Lasso in the prediction model. The CatBoost Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention (CNN-BiLSTM-Attention) model was used to forecast WQI. The research results propose a new WRCC evaluation and water quality prediction model method. The results show that the average barrier levels for water resources, socio-economic development, and ecological environment were 34.97%, 34.93%, and 30.10%, respectively. Compared to other system layers of WRCC, the obstacle degree of the ecological environment system layer has always been lower. The total sewage treatment, greening coverage in built-up areas, and per capita green space in parks were the main obstacle factors to the WRCC within the Min River Basin. Based on the results of the key factor screening, it can be seen that dissolved oxygen is positively correlated with the water quality of the watershed, while the other key influencing factors are negatively correlated with the WQI. Total nitrogen had the greatest impact on water quality conditions in the watershed, with a regression coefficient of −1.7532. From the comparison of the prediction results, it is known that the hybrid model can make the MAE value of 45% monitoring points reach the minimum, and the RMSE value of 35% monitoring points reach the minimum. The percentages of the remaining prediction models that reached the lowest values for MAE and RMSE were 15% to 20% and 15% to 30%, respectively. Compared with other prediction models, the MSE and RMSE values of the hybrid model were relatively small, which was more conducive to predicting water quality in the Min River Basin.