多测站水文变分数据同化：使用多层感知机和贝叶斯引导多元回归的空间梯度区域化学习
Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression
AI总结：
本文提出一种面向高分辨率水文模型的无缝区域化学习方法，通过多层感知机或贝叶斯引导的多元回归构建区域传递函数，并结合变分数据同化与多测站空间梯度优化，解决无测站水道洪水参数估计难题。
摘要：
针对估计空间分布水文参数的困难问题，尤其是无测站水道的洪水问题，本文提出了一种新颖的无缝区域化技术，用于学习为高分辨率水文模型设计的复杂区域传递函数。该传递函数依赖于：(i) 多层感知机，能够实现梯度计算的无缝流动，从而采用机器学习优化算法；或 (ii) 由变分数据同化算法优化并以贝叶斯估计为引导的多元回归映射，以解决可行解的异参同效性问题。该方法将可推断的区域化映射纳入可微水文模型，并利用基于伴随的精确空间分布梯度，优化在多测站数据上计算的代价函数。
英文摘要：
Tackling the difficult problem of estimating spatially distributed hydrological parameters, especially for floods on ungauged watercourses, this contribution presents a novel seamless regionalization technique for learning complex regional transfer functions designed for high-resolution hydrological models. The transfer functions rely on: (i) a multilayer perceptron enabling a seamless flow of gradient computation to employ machine learning optimization algorithms, or (ii) a multivariate regression mapping optimized by variational data assimilation algorithms and guided by Bayesian estimation, addressing the equifinality issue of feasible solutions. The approach involves incorporating the inferable regionalization mappings into a differentiable hydrological model and optimizing a cost function computed on multi-gauge data with accurate adjoint-based spatially distributed gradients.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
