面向美国本土的机器学习增强概念可解释流域尺度降水-存储-径流动力学建模
Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics
AI总结：
本研究利用质量守恒感知机（MCP）构建机器学习增强的物理可解释流域模型，在美国本土多种水文气候条件下评估模型性能，发现基于MCP的模型在性能上与LSTM相当，强调了根据水文过程优势选择合适模型复杂度的重要性。
摘要：
尽管许多现代研究致力于基于机器学习的大样本水文建模，但这些努力并未必然转化为基于增强的物理概念理解的预测改进。在此，我们报告了一项覆盖美国本土（跨越多种水文-地质-气候条件）的大样本研究，使用基于质量守恒感知机（MCP）的机器学习增强、物理可解释的流域尺度模型，模型复杂度各异。使用属性掩码（如雪情、森林覆盖和气候区）评估结果。我们的结果表明，根据过程优势随水文情势的变化选择适当复杂度的模型架构的重要性。基准比较显示，基于物理可解释的质量守恒MCP模型可以达到与基于长短期记忆网络（LSTM）架构的数据驱动模型相当的性能。总体而言，本研究强调了理论指导、物理基础方法在大样本水文学中的潜力，侧重于机制理解和简约可解释模型架构的开发，从而为未来能够编码空间和时间变化过程优势信息的通用模型奠定基础。
英文摘要：
While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded in enhanced physical-conceptual understanding. Here, we report on a CONUS-wide large-sample study (spanning diverse hydro-geo-climatic conditions) using ML-augmented physically-interpretable catchment-scale models of varying complexity based in the Mass-Conserving Perceptron (MCP). Results were evaluated using attribute masks such as snow regime, forest cover, and climate zone. Our results indicate the importance of selecting model architectures of appropriate model complexity based on how process dominance varies with hydrological regime. Benchmark comparisons show that physically-interpretable mass-conserving MCP-based models can achieve performance comparable to data-based models based in the Long Short-Term Memory network (LSTM) architecture. Overall, this study highlights the potential of a theory-informed, physically grounded approach to large-sample hydrology, with emphasis on mechanistic understanding and the development of parsimonious and interpretable model architectures, thereby laying the foundation for future models of everywhere that architecturally encode information about spatially- and temporally-varying process dominance.
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