论文标题
概率逆建模：水文应用
Probabilistic Inverse Modeling: An Application in Hydrology
论文作者
论文摘要
这些方法的惊人成功使得必须从这些模型中获得更可解释和值得信赖的估计。在水文学中，盆地特征可能是嘈杂的或缺失的，从而影响了流量的预测。为了解决此类应用中的反问题，确保解释性对于解决与数据偏差和较大搜索空间有关的问题至关重要。我们提出了一个概率的逆模型框架，该框架可以从动态输入天气驱动器和流量响应数据中重建健壮的水文流域特征。我们讲述了建立更可解释的逆模型，不确定性估计和鲁棒性的两个方面。这可以帮助提高水管理人员的信任，处理嘈杂的数据并降低成本。我们提出了基于不确定性的学习方法，从反向模型推断出盆地特征估计值的流量预测（正向建模）的$ r^2 $中，不确定性降低了17 \％（在存在噪声的情况下为40 \％），而盆地特征的覆盖率提高4 \％。
The astounding success of these methods has made it imperative to obtain more explainable and trustworthy estimates from these models. In hydrology, basin characteristics can be noisy or missing, impacting streamflow prediction. For solving inverse problems in such applications, ensuring explainability is pivotal for tackling issues relating to data bias and large search space. We propose a probabilistic inverse model framework that can reconstruct robust hydrology basin characteristics from dynamic input weather driver and streamflow response data. We address two aspects of building more explainable inverse models, uncertainty estimation and robustness. This can help improve the trust of water managers, handling of noisy data and reduce costs. We propose uncertainty based learning method that offers 6\% improvement in $R^2$ for streamflow prediction (forward modeling) from inverse model inferred basin characteristic estimates, 17\% reduction in uncertainty (40\% in presence of noise) and 4\% higher coverage rate for basin characteristics.
