An enhanced multi-head attention-based LSTM model for forecasting the surface water quality index

ABSTRACT Forecasting the surface water quality is vital for environmental monitoring and ecological sustainability. Although existing statistical and machine learning methods have been applied deliberately to forecast water quality, they often do not utterly delineate its complex spatial and temporal dynamics promptly. This in turn limits ensuring the accuracy and reliability of predictions, which are indispensable for effective environmental management. In order to overcome these challenges, we develop a novel approach as a multi-head attention-based long short-term memory model, specifically designed to enhance predictive precision that can capture complex dependencies using water quality datasets more precisely for the very first time. The proposed model shows a significant improvement over existing machine learning and deep learning models, achieving around 5–8% more accuracy in water quality forecasting. These enhanced results suggest that the proposed approach is well-suited for large-scale environmental applications, offering a data-driven approach that the supports targeted intervention strategies appropriately and reliably. This work contributes finely to the advancement of automated water quality forecasting systems, aiding sustainable environmental management practices.