Reinforcement learning for watershed and aquifer management: a nationwide view in the country of Mexico with emphasis in Baja California Sur

Abstract

Reinforcement Learning (RL) is a method that teaches agents to make informed decisions in diverse environments through trial and error, aiming to maximize a reward function and discover the optimal Q-learning function for decision-making. In this study, we apply RL to a rule-based water management simulation, utilizing a deep learning approach for the Q-learning value function. The trained RL model can learn from the environment and make real-time decisions. Our approach offers an unbiased method for analyzing complex watershed scenarios, providing a reward function as an analytical metric while optimizing decision-making time. Overall, this work underscores RL’s potential in addressing complex problems, demanding exploration, sequential decision-making, and continuous learning. External variables such as policy shifts, which are not readily integrated into the model, can substantially influence outcomes. Upon establishing a model with the requisite minimal states and actions, the subsequent learning process is relatively straightforward, depending on the selection of appropriate RL model algorithms. Its application depends on the specific problem. The primary challenge in this modeling approach lies in model definition, specifically in devising agents and actions that apply to complex scenarios. Our specific example was designed to address recent decision-making challenges related to constructing dams due to water scarcity. We present two examples: one from a nationwide perspective in Mexico and the other focused on Baja California Sur, the state with the highest water stress. Our results demonstrate our capability to prioritize watersheds effectively for the most significant benefits, particularly dam construction.