Using explainable AI to predict algal bloom forecasting and real-time analysis of deteriorating water quality in aquatic ecosystems
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Sivaranjini Raja 1 * , Sharanya S 1 , Rajkumar V 2
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1 Department of Data Science and Business Systems, School of Computing, SRM Institute of Science & Technology, Kattankulathur, Chennai, INDIA2 Department of Computer Science and Engineering, Krishnasamy College of Engineering and Technology, Cuddalore, INDIA* Corresponding Author

Abstract

Water resource management, public health, and aquatic environments are facing serious challenges due to the increasing frequency of algal blooms and declining water quality. The effectiveness of conventional prediction models in real-time applications is limited, as they often lack transparency and fail to account for the complex interrelationships among environmental variables. This research presents an innovative framework leveraging explainable artificial intelligence to enable real-time environmental assessments and reliable prediction of algal blooms. The approach integrates model interpretation techniques such as Shapley additive explanations and local interpretable model-agnostic explanations with advanced machine learning methods, including hybrid model ensembles and deep learning techniques. By combining these methodologies, the framework offers valuable insights into the processes driving bloom formation and water quality degradation while delivering high forecasting accuracy. Enhanced early-warning systems are developed to enable timely interventions and promote sustainable water conservation practices. Experimental results on diverse datasets demonstrate that the proposed approach achieves a prediction accuracy exceeding 95%. Interpretability metrics highlight key environmental factors such as temperature, dissolved oxygen levels, and nutrient concentrations. This work bridges the gap between model transparency and predictive accuracy, fostering trust in artificial intelligence-driven solutions for water quality management and environmental protection.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EUR J SUSTAIN DEV RES, Volume 9, Issue 4, 2025, Article No: em0333

https://doi.org/10.29333/ejosdr/16863

Publication date: 01 Oct 2025

Online publication date: 03 Sep 2025

Article Views: 677

Article Downloads: 225

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