Integrated Analysis of Feature Selection and Deep Learning Models for Water Quality Forecasting Based on Meteorological Parameters

Various pollutants pose significant threats to river ecosystems. This issue is particularly critical in Taiwan, where the unique geography of short, rapid rivers makes water retention difficult, necessitating rigorous water quality monitoring. Given the complex, non-linear correlations between water quality and meteorological parameters, this study investigates the impact of different feature selection techniques and predictive models on water quality forecasting for eight rivers in Taoyuan. We utilized 14 meteorological and water quality inputs to predict six key indicators, including COD, DO, EC, NH3-N, ORP, and SS. The methodology compared four feature selection strategies—Pearson Correlation, Entropy Weight Method (EWM), Combined Weights, and Mutual Information—alongside four forecasting models: Seq2Seq LSTM, ANFIS, MLP, and Transformer.The feature selection results reveal that the Entropy Weight Method yielded the highest precision (R^2 =0.9336), surpassing the Pearson method (R^2 =0.9161). This indicates that prioritizing features based on information entropy effectively minimizes information loss during screening. Regarding predictive modeling, the Transformer model demonstrated superior stability and accuracy. While other models fluctuated, the Transformer consistently achieved the best performance with an MSE of approximately 14.86 (RMSE=3.855) and an accuracy of 82.52%, significantly outperforming the MLP and ANFIS models. This study concludes that integrating entropy-based feature selection with the Transformer model establishes a superior and highly accurate framework for water quality forecasting in Taoyuan's rivers.