Analysis of Prediction Confidence in Water Quality Forecasting Employing LSTM

Water quality prediction serves as an important foundation for risk control and the proactive management of the aquatic environment, and the Long Short-Term Memory (LSTM) network has gained recognition as an effective approach for achieving high-precision water quality predictions. However, despite its potential, there is a significant gap in the literature regarding the confidence analysis of its prediction accuracy and the underlying causes of variability across different water quality indicators and basins. To address this gap, the present study introduces a novel confidence evaluation method to systematically assess the performance of LSTM in predicting key water quality parameters, including ammonia nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), hydrogen ion concentration (pH), and total phosphorus (TP). This evaluation was conducted across three basins with distinct geographical, climatic, and water quality conditions: the Huangshui River Basin (HSB), the Haihe River Basin (HRB), and the Yangtze River Basin (YRB). The results of the confidence evaluation revealed that LSTM exhibited higher credibility in the Haihe River Basin compared to the Yangtze River Basin. Additionally, LSTM demonstrated greater accuracy and stability in predicting total phosphorus (TP) compared to other water quality indicators in both basins, with median NSE values of 0.71 in the HRB and 0.73 in the YRB. Additionally, the research demonstrated a linear relationship between the ability of LSTM models to predict the water quality and temporal autocorrelation as well as the cross-correlation coefficients of the water quality parameters. The coefficients of determination (R2) ranged from 0.59 to 0.85, with values of 0.59 and 0.79 for the YRB and 0.85 and 0.80 for the HRB, respectively. This finding underscores the importance of considering these correlation metrics when evaluating the reliability of LSTM-based predictions.