Predictive monitoring, identification, and control of Microcystis blooms in a drinking water source basin: An integrative artificial intelligence and bioinformatics approach.

Harmful algal blooms (HABs) pose serious challenges for drinking water utilities; however, conventional monitoring in drinking water source basins remains largely reactive. To address this, we present an integrative framework that combines artificial intelligence (AI) forecasting with strain-resolved bioinformatic diagnostics to enable proactive monitoring of bloom-causing HAB in drinking water source. The AI model employed environmental input features and was optimized, which achieved high predictive accuracy (up to 0.9 R2) with early warning capabilities (1 week lead time). Explainable AI pinpointed organic and nitrogen compounds as top predictors for HAB events. Phylogenomic analysis using short- and long-read sequencing elucidated the population structure, toxicological traits, and ecological adaptation of the bloom-causing Microcystis population (NRERC-214). Field surveys with bioinformatic analysis at a full-scale drinking water treatment plant found complete removal of Microcystis. The relative strengths and weaknesses of the individual units in removing Microcystis in the plant was validated with the phenotypic/genotypic features of Microcystis bioinformatically determined in this study. Overall, this study suggested the integration of a predictive surveillance and diagnosis framework into a source water management scheme, supporting a paradigm shift from reactive post-event management to proactive, risk-based control for drinking water.