publication . Preprint . 2017

A trans-disciplinary review of deep learning research for water resources scientists

Shen, Chaopeng;
Open Access English
  • Published: 06 Dec 2017
Abstract
Deep learning (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...
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Machine Learning
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