Development of XAI-Based Explainable Planning Management for Chl-a Reduction

This study presents an explainable artificial intelligence (XAI)-based explainable planning management (EPM) framework designed to provide interpretable prediction-driven insights for water quality management. Although deep learning models such as the multi-layer perceptron (MLP) effectively predict water quality indicators, they have limited interpretability and practical use. To address this limitation, Shapley additive explanations (SHAP) were applied to quantify each input feature’s contribution to model-predicted chlorophyll-a (Chl-a) values and to support the construction of scenario-based analyses. The proposed framework was applied at the Dasan water quality observation station in the Nakdong river basin, Republic of Korea. Daily water quality data from 2014 to 2023 were used for model training, and 2024 data were used for prediction. The model excluding turbidity achieved the lowest root mean squared error (RMSE) of 7.3922. Scenario analyses were performed by varying Chl-a(t−1) and major variables in 10% increments, guided by influence identified through SHAP analysis. Results indicated that pH, which had the highest Shapley value excluding Chl-a(t−1), was the most influential variable, reducing algal bloom warning occurrences by up to 34%. These results demonstrate that the proposed EPM framework enhances interpretability and supports the exploration of prediction-based planning strategies, without implying causal or mechanistic relationships among water quality variables.