Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China.

As the most water shortage and water polluted area in China, the water quality prediction is of utmost needed and important in Haihe River Basin for its water resource management. The long short-term memory (LSTM) has been a widely used tool for water quality forecast in recent years. The performance and adaptability of LSTM for water quality prediction of different indicators needs to be discussed before it adopted in a specific basin. However, literature contains very few studies on the comparative analysis of the various prediction accuracy of different water quality indicators and the causes, especially in Haihe River Basin. In this study, LSTM was employed to predict biochemical oxygen demand (BOD), permanganate index (COD), dissolved oxygen (DO), ammonia nitrogen (NH-N), total phosphorus (TP), hydrogen ion concentration (pH), and chemical oxygen demand digested by potassium dichromate (COD). According to results under 24 different input conditions, it is demonstrated that LSTMs present better predicting on BOD, COD, COD, and TP (median Nash-Sutcliffe efficiency reaching 0.766, 0.835, 0.837, and 0.711, respectively) than NH-N, DO, and pH (median Nash-Sutcliffe efficiency of 0.638, 0.625, and 0.229, respectively). Besides, the performance of LSTM to predict water quality is linearly related to the maximum value of temporal autocorrelation and cross-correlation coefficients of water quality indicators calculated by maximal information coefficient with the coefficients of determination of 0.79 to approximately 0.80. This study would provide new knowledge and support for the practical application and improvement of the LSTM in water quality prediction.