Simulations of human behavior in water resources systems are challenged by uncertainty in model structure and parameters. The increasing availability of observations describing these systems provides the opportunity to infer a set of plausible model structures using data‐driven approaches. This study develops a three‐phase approach to the inference of model structures and parameterizations from data: problem definition, model generation, and model evaluation, illustrated on a case study of land use decisions in the Tulare Basin, California. We encode the generalized decision problem as an arbitrary mapping from a high‐dimensional data space to the action of interest and use multiobjective genetic programming to search over a family of functions that perform this mapping for both regression and classification tasks. To facilitate the discovery of models that are both realistic and interpretable, the algorithm selects model structures based on multiobjective optimization of (1) their performance on a training set and (2) complexity, measured by the number of variables, constants, and operations composing the model. After training, optimal model structures are further evaluated according to their ability to generalize to held‐out test data and clustered based on their performance, complexity, and generalization properties. Finally, we diagnose the causes of good and bad generalization by performing sensitivity analysis across model inputs and within model clusters. This study serves as a template to inform and automate the problem‐dependent task of constructing robust data‐driven model structures to describe human behavior in water resources systems.
<p>Water infrastructure operations can adapt to both short-term variability and long-term change. Studies that have leveraged climate information to reoperate infrastructure have yet to explore the direct use of spatially distributed information in operating policy training, which could enable learning from weather patterns associated with emerging risks&#8212;for example, flood and drought events associated with atmospheric rivers or high-pressure ridges, respectively, which result from co-occurring weather and climate patterns on multiple timescales. This study investigates the potential for spatial projections from large-ensemble climate models to directly inform reservoir operating policies using a deep reinforcement learning strategy, aiming to discover flexible, climate-informed policies without prior dimension reduction, which could cause loss of information. The approach is demonstrated for Folsom Reservoir in California. We investigate how learned policies interpret spatial climate information by connecting flood control and water supply shortage operations to the sensitivity and salience patterns associated with the input images. To assess the extent to which trained policies generalize to possible future climates, policies trained on historical data are tested on held-out scenarios drawn from the same period, and their performance is compared to flood and shortage scenarios drawn from a future period. Trained policies are robust to the variability present across climate model ensembles, demonstrate value in identifying spatial climate patterns for operations, and maintain the flexibility to dynamically adapt to climate change as it occurs, illustrating a broad benefit to global infrastructure systems facing climate risks.</p>
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