Enhancing Water Quality Forecasting with Lstm and Attention-based Deep Learning: Toward Scalable, Long-term Monitoring of Surface Water Bodies

Memory Marozva; Munyaradzi Rushambwa; Peter BukhelaniMusiiwa

Enhancing Water Quality Forecasting with Lstm and Attention-based Deep Learning: Toward Scalable, Long-term Monitoring of Surface Water Bodies

Author(s)	Memory Marozva, Munyaradzi Rushambwa, Peter BukhelaniMusiiwa
Country	Zimbabwe
Abstract	Water systems, such as lakes, rivers, and dams, are essential to a nation's socioeconomic growth, agricultural output, and ecological balance. Numerous industries, including hydropower, mining, fishing, agriculture, and recreation, are supported by these resources. However, issues like climate change, which includes extreme droughts, unpredictable rainfall patterns, and industrial pollution, as well as the nation's heavy reliance on water—90% of the world's supply—are posing an increasing threat to these water bodies, water reservoirs, and their sustainability. The degradation of these water bodies brought on by climate change has had a detrimental effect on agriculture and food security in particular. The greatest water quality prediction and management systems that can assist and advise stakeholders in implementing the required and effective strategies to conserve and maintain high-quality water bodies have been developed through the use and testing of numerous and varied approaches. An artificial intelligence (AI)-based predictive analysis framework for proactive water conservation and quality assurance is reviewed and examined in this study. The study incorporates data from multiple machine learning approaches to show that hybrid models, including CNN-LSTM-Attention networks, perform well in predicting water quality indicators like pH, dissolved oxygen, and electrical conductivity (EC). This paper's work demonstrates how the AI-based predictive analytic system was created as a means of developing a more complete, accurate, and effective system. Experimental data was used to validate the created framework, and the results illustrate a promising correlation between the actual and projected water conditions. This strategy lays the groundwork for more intelligent water management systems, especially in emerging nations where urbanization and climate change are putting strain on water infrastructure. The findings support the idea that hybrid AI systems could improve environmental governance, strengthen early warning systems, and improve resource allocation. Using tree-based models like Random Forest, recent AI-based water quality monitoring systems have effectively predicted short-term changes. However, multivariate relationships and nonlinear seasonal patterns make predicting over longer time periods difficult. In order to forecast pH, turbidity, and dissolved oxygen levels up to 30 days ahead of time, this study proposes a hybrid deep learning model that combines Long Short-Term Memory (LSTM) networks with an attention mechanism. By using enhanced datasets from several freshwater bodies, the model outperforms conventional techniques in terms of accuracy. The model's ability to capture dynamic fluctuations and long-term dependencies in environmental data is validated by the results, which make a compelling case for incorporating deep learning into frameworks for global surface water monitoring. By advancing predictive capabilities, this research contributes directly to Sustainable Development Goal 6 (Clean Water and Sanitation), supporting smarter governance and public health interventions across the globe.[1]
Keywords	Water Quality Forecasting, Deep Learning, LSTM, Attention Mechanism, Environmental Monitoring, Surface Water Bodies
Field	Engineering
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-08-01

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Enhancing Water Quality Forecasting with Lstm and Attention-based Deep Learning: Toward Scalable, Long-term Monitoring of Surface Water Bodies

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