Reinforcement-Learning Based Optimal Scheduling of Multi-Reservoir Irrigation Systems in Semi-Arid Regions

Abstract

Multi-reservoir irrigation systems are vital for ensuring agricultural production and food security in temperate and semi-arid regions. Reservoir-reservoir interactions must be considered, since water is released from different reservoirs to irrigation networks with different priorities. Reinforcement-learning methods provide a framework to establish such interactions, but their performance has not been compared with heuristic and model-based methods in a multi-reservoir irrigation context. Here, a multi-reservoir irrigation system is modeled and controlled with a reinforcement-learning approach that is evaluated against two routing policies and two techniques from the RL literature. The reward signal favours high water-use efficiency, penalizes missed irrigation demand, and rewards reliable supply. The explorative agent learns the complete operational policy, including transfers among reservoirs, directly from simulation. The results reveal the RL agent's superior ability to maximize water-use efficiency with acceptable deficits.

The proposed method also proves robust against drought conditions, delivering reliable schedules under severe alterations of inflow, unrestricted by time. The need to allocate limited water resources efficiently has pushed the development of different water distribution methods, each with pros and cons. These might still be shaped by reinforcement-learning techniques that move toward real-time decision-making and climate-adaptive management, paving the way for an intelligent water-resources management system able to interact with the hydrological model of an irrigation system.