A Data-Driven Framework for Spatiotemporal Analysis and Prediction of River Water Quality: A Case Study in Pearl River, China

Abstract

Characterization of the spatiotemporal water quality variation is of utmost importance for water resource management. Changes in water quality have been shown to be significantly affected by uncertain factors such as environmental conditions and anthropogenic activities. However, few studies consider the impact of these variables on water quality prediction while developing statistical methods or machine learning algorithms. To solve the problem, a data-driven framework for the analysis and prediction of water quality in the Guangzhou reach of the Pearl River, China, was constructed in this study. The results provided evidence of a discrepancy in the spatiotemporal dynamics of water quality, with the average water quality index (WQI) values ranging from 52.47 to 83.06, implying “moderate” to “excellent” water quality at different stations. Environmental conditions and anthropogenic activities exerted great influence on the alteration of water quality, with correlation coefficients of 0.6473–0.7903. The relevant environmental factors and anthropogenic drivers combined with water quality variables were taken into account to establish the attention-based long short-term memory (LSTM-attention) model. The proposed LSTM-attention model achieved reliable real-time water quality prediction with up to a 3-day lead-time and a determination coefficient (R2) of 0.6. The proposed hybrid framework sheds light on the development of a decision system for comprehensive water resource management and early control of water pollution.