A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists

By Chaopeng Shen. Water Resources Research, 2017

956 citations recorded by the source corpus.

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 interdisciplinarity, 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, transdisciplinary 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 subdiscipline 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.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/b7e5cca32ea7d7ae8e095a11549f6c7918148b15

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Citations in the corpus

Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems (2020, background): “…approaches and the successes of machine learning in solving problems in ESM (Gentine et al., 2018) or offline hydrologic model (Fang et al., 2017; Shen, 2018) behavior, designing models with an emphasis on modular complexity to allow for such hybrid approaches is a crucial challenge in modeling…”
A review of machine learning applications in wildfire science and management (2020, background): “(2018), extreme weather prediction McGovern et al. (2017), flood forecasting Mosavi et al. (2018), statistical downscaling Vandal, Kodra, and Ganguly (2018), remote sensing Lary, Alavi, Gandomi, and Walker (2016), and water resources Shen (2018); Sun and Scanlon (2019).”
Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media (2018, background): “To further improve the computational efficiency and accuracy, one can increase the network depth (Shen, 2018; Simonyan & Zisserman, 2014).”
Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “5 or PM10 levels by using satellite observations and station measurements (Li et al., 2017; Shen et al., 2018; Tang et al., 2018).”
Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales (2019, background): “1 of 24 Recently, time‐series deep learning (DL) has emerged as a powerful and versatile modeling tool in hydrology (Fang et al., 2017; Kratzert et al., 2018; Shen, 2018; Shen et al., 2018), but few studies have examined streamflow forecast with DL, especially at large scales.”
A review of the application of machine learning in water quality evaluation (2022, methodology): “This is inferior to the BWNN model, which is inspired by the self-adaption in the learning process of the ANN and the time-frequency properties of the wavelet basis functions [18], and the LSTM model, a type of recurrent neural network structure, which learns directly from time-series data [39].”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, methodology): “[37] Deep learning Water resources related problems General discussion [38] ARIMA, ARMAX, linear regression, support vector machine, genetic programming, fuzzy logic, hybrid models Univariate streamflow forecasting General evaluation of the algorithms”
The role of deep learning in urban water management: A critical review. (2022, methodology): “At the same time, deep learning has started to substantially transform some scientific disciplines, such as high-energy physics, astronomy and computational biology (Shen, 2018), and is already transforming water research as reviewed in Section 3.”