Future-informed FOCAL MODE: a multimodal deep learning framework for forecasting dissolved oxygen in reservoirs

Area of Science:

• Environmental Science
• Water Resource Management
• Artificial Intelligence

Background:

• Dissolved oxygen (DO) is vital for reservoir health, but dam operations often reduce it.
• Current mitigation strategies like liquid oxygen injection are costly and require extensive planning.
• Accurate long-range DO forecasting is essential for effective and economical oxygenation efforts.

Purpose of the Study:

• To develop an advanced predictive modeling framework for accurate, long-range dissolved oxygen (DO) forecasting in reservoirs.
• To integrate data-driven learning with physics-based insights for robust DO predictions.
• To enhance climate responsiveness in DO forecasting models.

Main Methods:

• Designed Future-informed FOurier, Cross-modal Attention LSTM using Multi-modal Observational Data Engine (FOCAL MODE), a dual-channel predictive model.
• Channel 1: Learns from multi-dimensional historical environmental datasets capturing long-term patterns.
• Channel 2: Integrates physics-informed models incorporating future climate change projections.

Main Results:

• The Future-informed FOCAL MODE demonstrated strong accuracy in predicting DO levels, with RMSE ~1 in a Watts Bar Dam case study.
• The model provides resilient and climate-aware DO forecasts, effectively integrating historical data with future environmental scenarios.
• The dual-channel approach successfully combined retrospective learning with anticipatory modeling for enhanced predictive performance.

Conclusions:

• Future-informed FOCAL MODE offers a significant advancement in reservoir water quality management through accurate DO forecasting.
• The framework's ability to adapt historical learning to future trends enhances its utility in a changing climate.
• This approach enables timely and cost-effective mitigation of low DO events, supporting reservoir ecosystem health.