Water Quality Prediction Model Based on Tucker Decomposition and GRU-Attention Mechanism

Abstract

The issue of water pollution critically affects all living beings. The implementation of a smart water quality monitoring system, based on the Internet of Things, enables advancements in efficiency, security, and cost-effectiveness while providing real-time capabilities. Current water quality prediction models often fail to fully utilize data characteristics shared by water quality indicators, resulting in poor predictive accuracy. This study introduces a novel water quality prediction model named TGMHSA, which utilizes tensor decomposition combined with a gated neural network and a multi-head self-attention mechanism. The aim is to tackle the difficulty of forecasting water quality indicators using time series data while minimizing the risk of plagiarism. The proposed model utilizes standard delay embedding transformation (SDET) to convert the time series data into tensor data, extracting data characteristics by Tucker tensor decomposition, and then combines a multi-head self-attention mechanism to discover potential relationships among data characteristics of multiple water quality indicators. Finally, the utilization of the GRU model enables accurate prediction of multi-index water quality. In order to compare its performance, we consider four indices: root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination represented as R2. The outcomes demonstrate that this model outperforms traditional methods for predicting water quality in terms of accuracy and resilience, thereby establishing a scientific foundation for effective water quality prediction and environmental monitoring management.