Abstract

The degradation of natural water resources, including rivers, reservoirs, and lakes, represents one of the most pressing environmental challenges today. Effective water quality management is essential to ensure sustainable utilization of these vital resources. Conventional machine learning methods often face limitations such as sparse and irregular sampling, as most water quality monitoring stations record data infrequently, typically on a monthly basis. Additionally, traditional optimization algorithms relying on random partitioning and cross-validation can produce imbalanced sample distributions, resulting in suboptimal prediction performance during testing. To address these challenges, this study proposes a novel Hybrid Whale Optimization with Long Short-Term Memory and Attention Mechanism (HWOA-LSTM-Attention) framework for accurate water quality forecasting. The framework leverages LSTM networks to capture temporal dependencies and incorporates an attention mechanism to assign adaptive weights to critical features, thereby enhancing predictive accuracy for complex and nonlinear water quality parameters. The Hybrid Whale Optimization Algorithm (HWOA) is employed to fine-tune model hyperparameters, optimizing performance metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Absolute Proportion Error (APEmax), and the coefficient of determination (R²). Experimental results show that the proposed HWOA-LSTM-Attention framework achieves a high prediction accuracy of 96.84%, outperforming existing benchmark models. The approach enables water management authorities to forecast pollution levels more effectively, supporting early warning systems, disaster prevention, and real-time monitoring of pollutant dispersion across extensive water supply networks. This framework thus provides a robust, data-driven solution for sustainable and proactive water quality management.

Authors

D. Justin Jose, R. Jemila Rose, I. Jessy Mol, S. Anu Priyadharsini
St. Xavier’s Catholic College of Engineering, India

Keywords

Sustainable Water Management, Hybrid Optimization, Deep Learning, Water Quality Prediction, Resource Allocation