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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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Future-informed FOCAL MODE: a multimodal deep learning framework for forecasting dissolved oxygen in reservoirs.

Faezeh Bagheri1, Jon Hathaway2, Nathan Michael Barber3

  • 1Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, USA.

Environmental Monitoring and Assessment
|March 30, 2026
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Summary

Accurate long-range dissolved oxygen (DO) forecasts are crucial for reservoir management. A new model, Future-informed FOCAL MODE, combines historical data with physics-informed climate projections for improved predictions.

Keywords:
Deep learningDissolved oxygen predictionMulti-modal data integrationPhysics-informed modelingTime series analysis

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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.