Using explainable AI to predict algal bloom forecasting and real-time analysis of deteriorating water quality in aquatic ecosystems

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.