Water management has traditionally relied on reactive measures—when rainfall increases, dams open to regulate water levels. But what if this process could be optimized using advanced algorithms? Alan Tehan and his team at Technical Services in Syracuse, Indiana, are exploring just that.

With a strong background in automotive control and engine systems, Tehan’s team is investigating predictive modeling as a tool for refining dam operations.

The Current Approach

At present, dam operations follow a simple formula: rain arrives, the dam opens, and water levels are adjusted. While this reactive method works, Tehan believes it may not be the most efficient. By leveraging existing data—like historical rainfall, inflow rates, sun exposure, and local water usage—he suggests we can predict and preemptively manage dam activity with greater precision.

Applying Engine Control Principles to Water Management

Technical Services Inc. is known for its work in electronic control systems, especially in the automotive sector. Modern engines use predictive algorithms to optimize fuel efficiency and performance. Tehan sees an opportunity to apply similar logic to dams, using data-driven modeling to anticipate inflow patterns and adjust dam controls accordingly.

Building the Predictive Model

The foundation of this innovation lies in comprehensive data:

• 50 years of historical rainfall trends
• 10-year patterns of seasonal shifts
• Recent data from the past year
• Additional environmental factors like sprinkler use and sunlight exposure

This rich data set supports the development of models that forecast inflow changes before they occur.

Despite its promising potential, this process will take time. The dam is currently functioning properly, so the goal isn’t to replace manual operations but to enhance efficiency. Initial testing would involve running predictive algorithms alongside manual operations for at least a year or more, observing their accuracy before implementation.

Prioritizing Safety and Manual Control

Caution is key in any shift toward automation, and this project is no exception. Observations will take priority before any adjustments to dam operations are made. Even if implemented, a manual override will remain intact, ensuring that unexpected scenarios don’t disrupt water management. One possible improvement could involve a more incremental approach—rather than waiting for a major rainfall event to open the dam by several inches, predictive modeling could facilitate smaller, controlled adjustments over time. This could lead to smoother water level fluctuations, improving conditions for those who rely on the lake.

The Road Ahead

While predictive modeling in water management is still an emerging concept, the potential is promising. If successful, this initiative could set a precedent for more proactive dam control strategies, ultimately leading to better-managed reservoirs and more efficient use of natural resources.