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Title: HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

Abstract

Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens.Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn,stimulatemore » advances in machine learning as well.« less

Authors:
 [1];  [2]; ORCiD logo [3];  [4]; ORCiD logo [5];  [6];  [7];  [8];  [1]; ORCiD logo [9];  [1];  [9];  [10];  [1]
  1. Pennsylvania State Univ., University Park, PA (United States)
  2. Belgian Nuclear Research Centre, Mol (Belgium)
  3. Univ. of Saskatchewan, Saskatoon, SK (Canada)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  5. Consortium of Univ. for the Advancement of Hydrologic Science, Inc. (CUAHSI), Cambridge, MA (United States)
  6. National Taiwan Univ., Taipei (Taiwan)
  7. NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States)
  8. Univ. of California, Irvine, CA (United States)
  9. Univ. of Texas, Arlington, TX (United States)
  10. Sichuan Univ., Sichuan (China)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Science Foundation (NSF)
OSTI Identifier:
1542338
Grant/Contract Number:  
AC02-05CH11231; SC0016605; EAR-1832294; EAR-1338606; 2018SZ0343; CCF-1317560
Resource Type:
Accepted Manuscript
Journal Name:
Hydrology and Earth System Sciences (Online)
Additional Journal Information:
Journal Name: Hydrology and Earth System Sciences (Online); Journal Volume: 22; Journal Issue: 11; Journal ID: ISSN 1607-7938
Publisher:
European Geosciences Union (EGU)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Shen, Chaopeng, Laloy, Eric, Elshorbagy, Amin, Albert, Adrian, Bales, Jerad, Chang, Fi-John, Ganguly, Sangram, Hsu, Kuo-Lin, Kifer, Daniel, Fang, Zheng, Fang, Kuai, Li, Dongfeng, Li, Xiaodong, and Tsai, Wen-Ping. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community. United States: N. p., 2018. Web. doi:10.5194/hess-22-5639-2018.
Shen, Chaopeng, Laloy, Eric, Elshorbagy, Amin, Albert, Adrian, Bales, Jerad, Chang, Fi-John, Ganguly, Sangram, Hsu, Kuo-Lin, Kifer, Daniel, Fang, Zheng, Fang, Kuai, Li, Dongfeng, Li, Xiaodong, & Tsai, Wen-Ping. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community. United States. doi:10.5194/hess-22-5639-2018.
Shen, Chaopeng, Laloy, Eric, Elshorbagy, Amin, Albert, Adrian, Bales, Jerad, Chang, Fi-John, Ganguly, Sangram, Hsu, Kuo-Lin, Kifer, Daniel, Fang, Zheng, Fang, Kuai, Li, Dongfeng, Li, Xiaodong, and Tsai, Wen-Ping. Thu . "HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community". United States. doi:10.5194/hess-22-5639-2018. https://www.osti.gov/servlets/purl/1542338.
@article{osti_1542338,
title = {HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community},
author = {Shen, Chaopeng and Laloy, Eric and Elshorbagy, Amin and Albert, Adrian and Bales, Jerad and Chang, Fi-John and Ganguly, Sangram and Hsu, Kuo-Lin and Kifer, Daniel and Fang, Zheng and Fang, Kuai and Li, Dongfeng and Li, Xiaodong and Tsai, Wen-Ping},
abstractNote = {Recently, deep learning (DL) has emerged as a revolutionary and versatile tool transforming industry applications and generating new and improved capabilities for scientific discovery and model building. The adoption of DL in hydrology has so far been gradual, but the field is now ripe for breakthroughs. This paper suggests that DL-based methods can open up a complementary avenue toward knowledge discovery in hydrologic sciences. In the new avenue, machine-learning algorithms present competing hypotheses that are consistent with data. Interrogative methods are then invoked to interpret DL models for scientists to further evaluate. However, hydrology presents many challenges for DL methods, such as data limitations, heterogeneity and co-evolution, and the general inexperience of the hydrologic field with DL. The roadmap toward DL-powered scientific advances will require the coordinated effort from a large community involving scientists and citizens.Integrating process-based models with DL models will help alleviate data limitations. The sharing of data and baseline models will improve the efficiency of the community as a whole. Open competitions could serve as the organizing events to greatly propel growth and nurture data science education in hydrology, which demands a grassroots collaboration. The area of hydrologic DL presents numerous research opportunities that could, in turn,stimulate advances in machine learning as well.},
doi = {10.5194/hess-22-5639-2018},
journal = {Hydrology and Earth System Sciences (Online)},
number = 11,
volume = 22,
place = {United States},
year = {2018},
month = {11}
}

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Works referenced in this record:

Deep learning
journal, May 2015

  • LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
  • Nature, Vol. 521, Issue 7553
  • DOI: 10.1038/nature14539

Modeling Urbanization Patterns with Generative Adversarial Networks
conference, July 2018

  • Albert, Adrian; Strano, Emanuele; Kaur, Jasleen
  • IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
  • DOI: 10.1109/IGARSS.2018.8518032

Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
journal, August 2016

  • Yu, Kun-Hsing; Zhang, Ce; Berry, Gerald J.
  • Nature Communications, Vol. 7, Issue 1
  • DOI: 10.1038/ncomms12474

Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions
journal, November 2011

  • Delle Monache, Luca; Nipen, Thomas; Liu, Yubao
  • Monthly Weather Review, Vol. 139, Issue 11
  • DOI: 10.1175/2011MWR3653.1

Training‐Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network
journal, January 2018

  • Laloy, Eric; Hérault, Romain; Jacques, Diederik
  • Water Resources Research, Vol. 54, Issue 1
  • DOI: 10.1002/2017WR022148

Improvements to a MODIS global terrestrial evapotranspiration algorithm
journal, August 2011

  • Mu, Qiaozhen; Zhao, Maosheng; Running, Steven W.
  • Remote Sensing of Environment, Vol. 115, Issue 8
  • DOI: 10.1016/j.rse.2011.02.019

Systematic Bias in Land Surface Models
journal, October 2007

  • Abramowitz, Gab; Pitman, Andy; Gupta, Hoshin
  • Journal of Hydrometeorology, Vol. 8, Issue 5
  • DOI: 10.1175/JHM628.1

A glimpse beneath earth's surface: GLobal HYdrogeology MaPS (GLHYMPS) of permeability and porosity
journal, June 2014

  • Gleeson, Tom; Moosdorf, Nils; Hartmann, Jens
  • Geophysical Research Letters, Vol. 41, Issue 11
  • DOI: 10.1002/2014GL059856

Regional flood inundation nowcast using hybrid SOM and dynamic neural networks
journal, November 2014


Time-variable gravity from GRACE: First results: TIME-VARIABLE GRAVITY FROM GRACE
journal, June 2004

  • Wahr, John; Swenson, Sean; Zlotnicki, Victor
  • Geophysical Research Letters, Vol. 31, Issue 11
  • DOI: 10.1029/2004GL019779

Accuracy of GRACE mass estimates
journal, January 2006

  • Wahr, John; Swenson, Sean; Velicogna, Isabella
  • Geophysical Research Letters, Vol. 33, Issue 6
  • DOI: 10.1029/2005GL025305

Global patterns of drought recovery
journal, August 2017

  • Schwalm, Christopher R.; Anderegg, William R. L.; Michalak, Anna M.
  • Nature, Vol. 548, Issue 7666
  • DOI: 10.1038/nature23021

HydroShare: Sharing Diverse Environmental Data Types and Models as Social Objects with Application to the Hydrology Domain
journal, October 2015

  • Horsburgh, Jeffery S.; Morsy, Mohamed M.; Castronova, Anthony M.
  • JAWRA Journal of the American Water Resources Association, Vol. 52, Issue 4
  • DOI: 10.1111/1752-1688.12363

Soil–landscape modeling across a physiographic region: Topographic patterns and model transportability
journal, July 2006


FloodEye: Real-time flash flood prediction system for urban complex water flow
conference, October 2016


rosetta : a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions
journal, October 2001


Unidirectional long short-term memory recurrent neural network with recurrent output layer for low-latency speech synthesis
conference, April 2015

  • Zen, Heiga; Sak, Hasim
  • ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • DOI: 10.1109/ICASSP.2015.7178816

Probabilistic Weather Prediction with an Analog Ensemble
journal, October 2013

  • Delle Monache, Luca; Eckel, F. Anthony; Rife, Daran L.
  • Monthly Weather Review, Vol. 141, Issue 10
  • DOI: 10.1175/MWR-D-12-00281.1

Long Short-Term Memory
journal, November 1997


Artificial Neural Network Modeling of the Rainfall-Runoff Process
journal, October 1995

  • Hsu, Kuo-lin; Gupta, Hoshin Vijai; Sorooshian, Soroosh
  • Water Resources Research, Vol. 31, Issue 10
  • DOI: 10.1029/95WR01955

Deep learning for visual understanding: A review
journal, April 2016


The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
journal, April 1998

  • Hochreiter, Sepp
  • International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 06, Issue 02
  • DOI: 10.1142/S0218488598000094

The future of Earth observation in hydrology
journal, January 2017

  • McCabe, Matthew F.; Rodell, Matthew; Alsdorf, Douglas E.
  • Hydrology and Earth System Sciences, Vol. 21, Issue 7
  • DOI: 10.5194/hess-21-3879-2017

Evaluating the Visualization of What a Deep Neural Network Has Learned
journal, November 2017

  • Samek, Wojciech; Binder, Alexander; Montavon, Gregoire
  • IEEE Transactions on Neural Networks and Learning Systems, Vol. 28, Issue 11
  • DOI: 10.1109/TNNLS.2016.2599820

Accelerating advances in continental domain hydrologic modeling: ACCELERATING ADVANCES IN CONTINENTAL HYDROLOGIC MODELING
journal, December 2015

  • Archfield, Stacey A.; Clark, Martyn; Arheimer, Berit
  • Water Resources Research, Vol. 51, Issue 12
  • DOI: 10.1002/2015WR017498

Hydrologic synthesis: Across processes, places, and scales: HYDROLOGIC SYNTHESIS
journal, February 2006


Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network
journal, November 2017

  • Fang, Kuai; Shen, Chaopeng; Kifer, Daniel
  • Geophysical Research Letters, Vol. 44, Issue 21
  • DOI: 10.1002/2017GL075619

Anatomy of an Enduring Gender Gap: The Evolution of Women’s Participation in Computer Science
journal, December 2016


Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions
journal, March 2016

  • Nearing, Grey S.; Mocko, David M.; Peters-Lidard, Christa D.
  • Journal of Hydrometeorology, Vol. 17, Issue 3
  • DOI: 10.1175/JHM-D-15-0063.1

The Soil Moisture and Ocean Salinity Mission - An Overview
conference, July 2008

  • Mecklenburg, S.; Kerr, Y.; Font, J.
  • IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium
  • DOI: 10.1109/IGARSS.2008.4779878

Advances in natural language processing
journal, July 2015


Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
journal, October 2017

  • Karpatne, Anuj; Atluri, Gowtham; Faghmous, James H.
  • IEEE Transactions on Knowledge and Data Engineering, Vol. 29, Issue 10
  • DOI: 10.1109/TKDE.2017.2720168

Speech recognition with deep recurrent neural networks
conference, May 2013

  • Graves, Alex; Mohamed, Abdel-rahman; Hinton, Geoffrey
  • ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • DOI: 10.1109/ICASSP.2013.6638947

A decade of Predictions in Ungauged Basins (PUB)—a review
journal, June 2013


Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest
journal, April 2018


Deep learning for computational biology
journal, July 2016

  • Angermueller, Christof; Pärnamaa, Tanel; Parts, Leopold
  • Molecular Systems Biology, Vol. 12, Issue 7
  • DOI: 10.15252/msb.20156651

Precipitation Identification with Bispectral Satellite Information Using Deep Learning Approaches
journal, May 2017

  • Tao, Yumeng; Gao, Xiaogang; Ihler, Alexander
  • Journal of Hydrometeorology, Vol. 18, Issue 5
  • DOI: 10.1175/JHM-D-16-0176.1

Use long short-term memory to enhance Internet of Things for combined sewer overflow monitoring
journal, January 2018


Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions
journal, August 2010


Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning: Improving Noah LSM Parameters
journal, November 2016

  • Chaney, Nathaniel W.; Herman, Jonathan D.; Ek, Michael B.
  • Journal of Geophysical Research: Atmospheres, Vol. 121, Issue 22
  • DOI: 10.1002/2016JD024821

Toward the camera rain gauge
journal, March 2015

  • Allamano, P.; Croci, A.; Laio, F.
  • Water Resources Research, Vol. 51, Issue 3
  • DOI: 10.1002/2014WR016298

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network
journal, December 2017


It takes a community to raise a hydrologist: the Modular Curriculum for Hydrologic Advancement (MOCHA)
journal, January 2012


A Two-Stage Deep Neural Network Framework for Precipitation Estimation from Bispectral Satellite Information
journal, February 2018

  • Tao, Yumeng; Hsu, Kuolin; Ihler, Alexander
  • Journal of Hydrometeorology, Vol. 19, Issue 2
  • DOI: 10.1175/JHM-D-17-0077.1

A Deep Neural Network Modeling Framework to Reduce Bias in Satellite Precipitation Products
journal, March 2016

  • Tao, Yumeng; Gao, Xiaogang; Hsu, Kuolin
  • Journal of Hydrometeorology, Vol. 17, Issue 3
  • DOI: 10.1175/JHM-D-15-0075.1

The German Traffic Sign Recognition Benchmark: A multi-class classification competition
conference, July 2011

  • Stallkamp, Johannes; Schlipsing, Marc; Salmen, Jan
  • 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose), The 2011 International Joint Conference on Neural Networks
  • DOI: 10.1109/IJCNN.2011.6033395

Vector-based navigation using grid-like representations in artificial agents
journal, May 2018


Methods of the Water-Energy-Food Nexus
journal, October 2015

  • Endo, Aiko; Burnett, Kimberly; Orencio, Pedcris
  • Water, Vol. 7, Issue 10
  • DOI: 10.3390/w7105806

Could Machine Learning Break the Convection Parameterization Deadlock?
journal, June 2018

  • Gentine, P.; Pritchard, M.; Rasp, S.
  • Geophysical Research Letters, Vol. 45, Issue 11
  • DOI: 10.1029/2018GL078202

Deep Classifiers from Image Tags in the Wild
conference, January 2015

  • Izadinia, Hamid; Russell, Bryan C.; Farhadi, Ali
  • Proceedings of the 2015 Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions - MMCommons'15
  • DOI: 10.1145/2814815.2814821

A unified approach for process‐based hydrologic modeling: 1. Modeling concept
journal, April 2015

  • Clark, Martyn P.; Nijssen, Bart; Lundquist, Jessica D.
  • Water Resources Research, Vol. 51, Issue 4
  • DOI: 10.1002/2015WR017198

The Soil Moisture Active Passive (SMAP) Mission
journal, May 2010


Changing ideas in hydrology — The case of physically-based models
journal, January 1989


Assessing the potential global extent of SWOT river discharge observations
journal, November 2014


Deep learning for computational chemistry
journal, March 2017

  • Goh, Garrett B.; Hodas, Nathan O.; Vishnu, Abhinav
  • Journal of Computational Chemistry, Vol. 38, Issue 16
  • DOI: 10.1002/jcc.24764

Image Style Transfer Using Convolutional Neural Networks
conference, June 2016

  • Gatys, Leon A.; Ecker, Alexander S.; Bethge, Matthias
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • DOI: 10.1109/CVPR.2016.265

Going deeper with convolutions
conference, June 2015


A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
journal, November 2018


The future of hydrology: An evolving science for a changing world: OPINION
journal, May 2010

  • Wagener, Thorsten; Sivapalan, Murugesu; Troch, Peter A.
  • Water Resources Research, Vol. 46, Issue 5
  • DOI: 10.1029/2009WR008906

Mode Detection in Online Handwritten Documents Using BLSTM Neural Networks
conference, September 2012

  • Indermuhle, Emanuel; Frinken, Volkmar; Bunke, Horst
  • 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR)
  • DOI: 10.1109/ICFHR.2012.232

TOPMODEL: A critique
journal, July 1997


Reconstruction of three-dimensional porous media using generative adversarial neural networks
journal, October 2017


Fusing Heterogeneous Data: A Case for Remote Sensing and Social Media
journal, December 2018

  • Wang, Han; Skau, Erik; Krim, Hamid
  • IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, Issue 12
  • DOI: 10.1109/TGRS.2018.2846199

Climate-vegetation-soil interactions and long-term hydrologic partitioning: signatures of catchment co-evolution
journal, January 2013

  • Troch, P. A.; Carrillo, G.; Sivapalan, M.
  • Hydrology and Earth System Sciences, Vol. 17, Issue 6
  • DOI: 10.5194/hess-17-2209-2013

Understanding deep image representations by inverting them
conference, June 2015

  • Mahendran, Aravindh; Vedaldi, Andrea
  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • DOI: 10.1109/CVPR.2015.7299155

Moving university hydrology education forward with community-based geoinformatics, data and modeling resources
journal, January 2012


Regression Shrinkage and Selection Via the Lasso
journal, January 1996


DeepQA Jeopardy! Gamification: A Machine-Learning Perspective
journal, March 2014

  • Baughman, Aaron K.; Chuang, Wesley; Dixon, Kevin R.
  • IEEE Transactions on Computational Intelligence and AI in Games, Vol. 6, Issue 1
  • DOI: 10.1109/TCIAIG.2013.2285651

Improved streamflow forecasting using self-organizing radial basis function artificial neural networks
journal, August 2004


The AI detectives
journal, July 2017


Development of a computationally efficient semi-distributed hydrologic modeling application for soil moisture, lateral flow and runoff simulation
journal, November 2016


A Big Data Guide to Understanding Climate Change: The Case for Theory-Guided Data Science
journal, September 2014


Creativity, Uncertainty, and Automated Model Building: Technical Commentary
journal, July 2017


Enhanced Higgs Boson to τ + τ Search with Deep Learning
journal, March 2015


“Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022
journal, July 2013


Detecting Arbitrary Oriented Text in the Wild with a Visual Attention Model
conference, January 2016

  • Huang, Wenyi; He, Dafang; Yang, Xiao
  • Proceedings of the 2016 ACM on Multimedia Conference - MM '16
  • DOI: 10.1145/2964284.2967282

Deep Residual Learning for Image Recognition
conference, June 2016

  • He, Kaiming; Zhang, Xiangyu; Ren, Shaoqing
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • DOI: 10.1109/CVPR.2016.90

Active Learning
journal, June 2012


DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
conference, January 2017

  • Vandal, Thomas; Kodra, Evan; Ganguly, Sangram
  • Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '17
  • DOI: 10.1145/3097983.3098004

Neural Error Regression Diagnosis (NERD): A Tool for Model Bias Identification and Prognostic Data Assimilation
journal, February 2006

  • Abramowitz, Gab; Gupta, Hoshin; Pitman, Andy
  • Journal of Hydrometeorology, Vol. 7, Issue 1
  • DOI: 10.1175/JHM479.1

Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps
journal, January 2018


Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
journal, December 2017

  • Zhu, Xiao Xiang; Tuia, Devis; Mou, Lichao
  • IEEE Geoscience and Remote Sensing Magazine, Vol. 5, Issue 4
  • DOI: 10.1109/MGRS.2017.2762307

Deep learning in neural networks: An overview
journal, January 2015


TOPMODEL: A critique
journal, July 1997


    Works referencing / citing this record:

    Advances in the Remote Sensing of Terrestrial Evaporation
    journal, May 2019

    • McCabe, Matthew F.; Miralles, Diego G.; Holmes, Thomas R. H.
    • Remote Sensing, Vol. 11, Issue 9
    • DOI: 10.3390/rs11091138

    Improving Precipitation Estimation Using Convolutional Neural Network
    journal, March 2019

    • Pan, Baoxiang; Hsu, Kuolin; AghaKouchak, Amir
    • Water Resources Research, Vol. 55, Issue 3
    • DOI: 10.1029/2018wr024090

    Advances in the Remote Sensing of Terrestrial Evaporation
    journal, May 2019

    • McCabe, Matthew F.; Miralles, Diego G.; Holmes, Thomas R. H.
    • Remote Sensing, Vol. 11, Issue 9
    • DOI: 10.3390/rs11091138

    Velocity Field Estimation on Density‐Driven Solute Transport With a Convolutional Neural Network
    journal, August 2019

    • Kreyenberg, Philipp J.; Bauser, Hannes H.; Roth, Kurt
    • Water Resources Research, Vol. 55, Issue 8
    • DOI: 10.1029/2019wr024833

    Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast
    journal, August 2019

    • de la Fuente, Alberto; Meruane, Viviana; Meruane, Carolina
    • Water, Vol. 11, Issue 9
    • DOI: 10.3390/w11091808

    Modelling of the shallow water table at high spatial resolution using random forests
    journal, January 2019

    • Koch, Julian; Berger, Helen; Henriksen, Hans Jørgen
    • Hydrology and Earth System Sciences, Vol. 23, Issue 11
    • DOI: 10.5194/hess-23-4603-2019

    Process‐Guided Deep Learning Predictions of Lake Water Temperature
    journal, November 2019

    • Read, Jordan S.; Jia, Xiaowei; Willard, Jared
    • Water Resources Research, Vol. 55, Issue 11
    • DOI: 10.1029/2019wr024922

    Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations
    journal, July 2019

    • Araya, Samuel N.; Ghezzehei, Teamrat A.
    • Water Resources Research, Vol. 55, Issue 7
    • DOI: 10.1029/2018wr024357

    An improved long short-term memory network for streamflow forecasting in the upper Yangtze River
    journal, February 2020

    • Zhu, Shuang; Luo, Xiangang; Yuan, Xiaohui
    • Stochastic Environmental Research and Risk Assessment, Vol. 34, Issue 9
    • DOI: 10.1007/s00477-020-01766-4

    Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
    journal, January 2018

    • Kratzert, Frederik; Klotz, Daniel; Brenner, Claire
    • Hydrology and Earth System Sciences, Vol. 22, Issue 11
    • DOI: 10.5194/hess-22-6005-2018

    Using Machine Learning for Prediction of Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural Perturbations
    journal, July 2019

    • Araya, Samuel N.; Ghezzehei, Teamrat A.
    • Water Resources Research, Vol. 55, Issue 7
    • DOI: 10.1029/2018wr024357

    Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification
    journal, May 2019

    • Mo, Shaoxing; Zabaras, Nicholas; Shi, Xiaoqing
    • Water Resources Research, Vol. 55, Issue 5
    • DOI: 10.1029/2018wr024638