Deep Learning for Trustworthy Prediction of Cyanobacterial Blooms across CONUS Inland Waters

Cyanobacterial harmful algal blooms (cHABs) pose serious concerns to drinking-water safety, aquatic ecosystem health, and recreational water use across the globe. cHABs in situ data collection relies on sparse and irregular measurements and hinders the reliable learning of complex ecological processes. Although recently data-driven models have improved bloom prediction skills, the explicit reliance on the black-box nature of these models undermines scientific trust and restricts the actionable value of model outputs. In this study, we developed a mechanism-aligned deep learning framework that embeds ecological process structure directly into the learning architecture using diverse data sources. We incorporated detailed remote-sensing-based atmospheric and environmental variables, including aerosol derived deposition, nutrient wet deposition, and meteorological data. We temporally aggregated this data to reflect both short-term forcing and cumulative conditions over space and time. We evaluated our framework for 2,200 lakes across the continental United States from 2018 - 2023, with a one-week-ahead bloom prediction task. Our model is trained on 2018–2021, validated in 2022, and tested on 2023 dataset. Our preliminary results show stable generalization under diverse spatiotemporal domain shifts (R² = 0.54, RMSE 0.59) with reduced seasonal bias relative to conventional deep learning baselines. In addition to predictive accuracy, our architecture demonstrates high explanation faithfulness (OTA = 0.83) and positive alignment with independent physical proxies (auxiliary physical proxies, R² = 0.36). This further demonstrates that architecture learned representations remain physically consistent despite the absence of direct mechanism labels. Our work advances a new paradigm for trustworthy environmental predictions and provides a novel foundation for actionable bloom management and policy decision support in data-limited inland water systems.