Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling
07 July 2020

Trans-Disciplinary Review of Deep Learning for Water Resources Scientists

Science

Introduction and review the progress of deep learning (DL) for water resources applications. Technical review of DL. Trans-disciplinary update on the use of DL for scientific advancement. Identify potential applications of utilizing DL to advance water sciences.

Impact

This paper has been cited >92 times since its publication in 2018. It inspired a large number of hydrologists as well as other earth scientists to examine the applicability of deep learning for their work.

Summary

Part of the Hyperion project requires the analysis of big climate and hydrologic data. A novel method that adds value to such data is deep learning (DL). DL, a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem‐relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as inter‐disciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, trans‐disciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image‐like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed “AI neuroscience,” where scientists interpret the decision process of deep networks and derive insights has been born. This budding sub‐discipline has demonstrated methods including correlation‐based analysis, inversion of network‐extracted features, reduced‐order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem‐specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

Contact
Chaopeng Shen
Pennsylvania State University