GA-based implicit stochastic optimization and RNN-based simulation for deriving multi-objective reservoir hedging...

Area of Science:

• Environmental Engineering
• Water Resource Management
• Artificial Intelligence in Hydrology

Background:

• Reservoir system management is complex due to uncertainties in future events and diverse operational objectives.
• Effective reservoir management is crucial for optimizing resource utilization and mitigating stakeholder conflicts.

Purpose of the Study:

• To develop an advanced optimization-simulation framework for reservoir operation.
• To address challenges in reservoir management using implicit stochastic optimization (ISO), genetic algorithms (GA), and recurrent neural network (RNN).

Main Methods:

• Generated synthetic monthly inflow scenarios for a multi-objective genetic programming model.
• Constructed an optimal operating rules database using genetic algorithms.
• Simulated monthly reservoir hedging rules with RNN, integrating inflow forecasts and the optimal rules database.

Main Results:

• The GA-ISO-RNN model demonstrated high accuracy in simulating and predicting optimal reservoir releases.
• RNN showed significant effectiveness, evidenced by high Nash-Sutcliffe and correlation coefficients, and low RMSE and MAD.
• The proposed model proved less vulnerable than standard operating rules when compared against historical releases.

Conclusions:

• The GA-ISO-RNN framework is effective for optimizing reservoir operations and predicting releases.
• The methodology offers a robust and accurate approach to water resource management.
• The model is adaptable for application to other reservoir systems globally.