Robust Ordinal Algal Alert Prediction Framework Integrating Heterogeneous Spatio-temporal Data and Dual Cross-Validation

Harmful Algal Blooms (HABs) pose a significant threat to freshwater ecosystems and public health globally, necessitating reliable early warning systems for effective water resource management. This study presents an end-to-end AI framework designed to predict 4-level ordinal algal alerts in South Korea by systematically integrating heterogeneous spatio-temporal environmental datasets, including GIS-based spatial features, water quality, meteorological, and hydrological data. Our methodological approach involves: (1) extracting spatial features via GIS; (2) optimizing time-lags and interpolating time-series data based on Spearman correlation; and (3) performing ordinal classification using a LightGBM (LGBM) model. To address the ordinal nature of algal alerts, the model was optimized using Optuna with the Quadratic Weighted Kappa (QWK) metric. A rigorous Dual Cross-Validation (CV) framework was employed to assess generalization capabilities: Year-over-Year (YoY) CV with an Embargo technique was used to evaluate temporal performance while preventing data leakage, and Leave-One-Station-Out (LOSO) CV was applied to validate spatial generalization for unobserved locations. Additionally, Isotonic Regression was implemented for probability calibration to enhance the reliability of the predicted outputs. By effectively controlling spatio-temporal information leakage, this study demonstrates superior predictive performance across unobserved timeframes and locations, providing a robust decision-support tool for practical water quality management.