Optimizing Irrigation Canal Operations: Advanced Machine Learning Approach

Efficient and equitable water distribution in large irrigation projects is crucial for sustainable agriculture. Inconsistent cross-regulator operations present significant challenges. We introduce an advanced quantitative model to standardize operations, ensuring reliable water supply management. We simulate numerous scenarios using the MIKE11 modeling system to generate extensive water level data. These data inform rule curves that define relationships between upstream and downstream water levels and discharge for specific gate settings. We validate these rule curves with field measurements. We also present the "Structure Operation System (SOS)," a tool that integrates data science and machine learning. This tool provides quick access to operational data, enhancing decision-making and improving irrigation efficiency. Our approach optimizes water management and promotes equitable access to water resources.