Water quality analysis based on LSTM and BP optimization with a transfer learning model

Abstract

In the urban water environmental management, a fast and effective method for water quality analysis should be established with the rapid urbanization. In this study, the Beijing's sub-center was chosen as a case study, and long short-term memory (LSTM) and back propagation (BP) models were built, then a transfer learning model was proposed and applied to optimize the two models on the base of the upstream and downstream relationships in the rivers. The results indicated that the proposed deep learning model could improve NSE by 7% and 9% for LSTM and BP at the Dongguan Bridge gauge, respectively. At the Xugezhuang gauge in the Liangshui River, NSE was improved by 11% and 17%, respectively. At the Yulinzhuang gauge, it was improved by 16% and 13%, respectively. Because the upstream and downstream relationships were considered in the learning model, the model performance was obviously better. In brief, this method would provide an idea for the effective water quality model construction in the ungauged basins or regions.

Keywords: BP; Beijing’s sub-center; Deep learning; LSTM; Water quality.