论文标题
不确定性估计，深度学习降雨跑步建模
Uncertainty Estimation with Deep Learning for Rainfall-Runoff Modelling
论文作者
论文摘要
深度学习正在成为越来越重要的方法，可以在各种空间和时间尺度上产生准确的水文预测。不确定性估计对于可行的水文预测至关重要，尽管标准化的社区基准正在成为水文模型开发和研究越来越重要的一部分，但缺乏基准测试不确定性估计的类似工具。我们建立了不确定性估计基准测试程序，并呈现四个深度学习基线，其中三个基于混合密度网络，一个基于蒙特卡洛辍学。此外，我们提供了事后模型分析，以提出对所得模型的一些定性理解。但是，最重要的是，我们表明可以通过深度学习来实现准确，精确和可靠的不确定性估计。
Deep Learning is becoming an increasingly important way to produce accurate hydrological predictions across a wide range of spatial and temporal scales. Uncertainty estimations are critical for actionable hydrological forecasting, and while standardized community benchmarks are becoming an increasingly important part of hydrological model development and research, similar tools for benchmarking uncertainty estimation are lacking. We establish an uncertainty estimation benchmarking procedure and present four Deep Learning baselines, out of which three are based on Mixture Density Networks and one is based on Monte Carlo dropout. Additionally, we provide a post-hoc model analysis to put forward some qualitative understanding of the resulting models. Most importantly however, we show that accurate, precise, and reliable uncertainty estimation can be achieved with Deep Learning.
