3 Geomorphological, Hydrologic, and Geothermal investigations of basin aquifers in Baja California Sur (BCS), Mexico

The session will explore BCS basins, focusing on groundwater storage, evaporation, runoff and transport mechanisms, featuring experts on thermal and shallow groundwater interactions, soil mapping for hydrological planning, geomorphology, and human impacts on water balance.

Wednesday, 22 April 2026
8:00 AM - 12:00 PM
LMH, Isla Carmen

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

Paper number: 3-11
Program: Technical Sessions
Time: 10:35 AM – 10:55 AM

Authors

Abstract

Reinforcement learning RL is a method that teaches agents to make informed decisions in diverse envirnoments through trial and error, aiming to maximize a reward function and discover the optimal Q - learning funcion for decision - making. In this study, we apply RL to a rule-based water manager 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 wathershed scenarios providing a reward function as an analytical metric while optimizing decision making time. Overall, this work underscore RL´s potential in addressing complex problems, demanding exploration, sequencial decision-making, and continuos 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 appropiate RL model alghorithms. Its application depends on the specific problem. The primary challenge in this modelling approach lies in model defintion, specifically in devising agents and actions that apply to complex scenarios. Our specific example was design to address recent decision-making challenges relating to constructed dams to 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 watershed effectively for the most significan benefits particularly dam construction.

Geological Society of America Abstracts with Programs. Vol. 58, No. 3

doi: 10.1130/abs/2026CD-410794

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