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.

中文翻译：

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面向数据驱动的水资源系统人类行为综合表示的生成和模型结构评估

模型结构和参数的不确定性对水资源系统中人类行为的模拟提出了挑战。描述这些系统的观测资料的可用性不断提高，提供了使用数据驱动的方法推断一组合理的模型结构的机会。这项研究开发了一种从数据推断模型结构和参数化的三阶段方法：问题定义，模型生成和模型评估，以加利福尼亚州图莱里盆地的土地使用决策案例研究为例。我们将广义决策问题编码为从高维数据空间到感兴趣动作的任意映射，并使用多目标遗传规划来搜索执行回归和分类任务映射的函数族。为了促进发现既现实又可解释的模型，该算法基于以下多目标优化选择模型结构：（1）模型在训练集上的性能和（2）复杂度（通过变量，常量和组成运算的数量来衡量）该模型。训练后，将根据最佳模型结构对测试数据进行泛化的能力进一步评估，并根据其性能，复杂性和泛化特性对其进行聚类。最后，我们通过对模型输入之间以及模型集群内进行敏感性分析来诊断普遍性好坏的原因。本研究作为模板，用于通知和自动化与问题相关的任务，以构建可靠的数据驱动的模型结构来描述水资源系统中的人类行为。