期刊
WATER RESOURCES RESEARCH
卷 57, 期 3, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2020WR028091
关键词
Machine Learning; Deep Learning; Uncertainty; Modeling
资金
- Google faculty research award
- NASA Advanced Information Systems Technology program [80NSSC17K0541]
- NASA Terrestrial Hydrology Program [80NSSC18K0982]
- Government of Cantabria through the Fnix Programme
This paper is derived from a keynote talk given at Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall-runoff simulation show that there is more information in large-scale hydrological data sets than previously thought. The paper calls for the hydrology community to focus on developing a quantitative understanding of the value of hydrological process understanding in a modeling discipline increasingly dominated by machine learning.
Y This paper is derived from a keynote talk given at the Google's 2020 Flood Forecasting Meets Machine Learning Workshop. Recent experiments applying deep learning to rainfall-runoff simulation indicate that there is significantly more information in large-scale hydrological data sets than hydrologists have been able to translate into theory or models. While there is a growing interest in machine learning in the hydrological sciences community, in many ways, our community still holds deeply subjective and nonevidence-based preferences for models based on a certain type of process understanding that has historically not translated into accurate theory, models, or predictions. This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning. We offer some potential perspectives and preliminary examples about how this might be accomplished.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据