Articles | Volume 22, issue 11
Hydrol. Earth Syst. Sci., 22, 5639–5656, 2018
https://doi.org/10.5194/hess-22-5639-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue: HESS Opinions 2018
Opinion article
01 Nov 2018
Opinion article
| 01 Nov 2018
HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Chaopeng Shen et al.
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- A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America L. Estupinan-Suarez et al. 10.3389/feart.2021.613395
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- Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks F. Kratzert et al. 10.5194/hess-22-6005-2018
- From Slide Rule to Big Data: How Data Science is Changing Water Science and Engineering J. Hering 10.1061/(ASCE)EE.1943-7870.0001578
- A framework for projecting future intensity-duration-frequency (IDF) curves based on CORDEX Southeast Asia multi-model simulations: An application for two cities in Southern Vietnam W. Zhao et al. 10.1016/j.jhydrol.2021.126461
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- Advent of Big Data technology in environment and water management sector J. Gohil et al. 10.1007/s11356-021-14017-y
- Lake water-level fluctuation forecasting using machine learning models: a systematic review S. Zhu et al. 10.1007/s11356-020-10917-7
- Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks G. Ayzel et al. 10.3390/hydrology8010006
- Unravelling groundwater time series patterns: Visual analytics-aided deep learning in the Namoi region of Australia S. Clark 10.1016/j.envsoft.2022.105295
- GMD perspective: The quest to improve the evaluation of groundwater representation in continental- to global-scale models T. Gleeson et al. 10.5194/gmd-14-7545-2021
- Toward catchment hydro‐biogeochemical theories L. Li et al. 10.1002/wat2.1495
- Development of enthalpy-based climate indicators for characterizing building cooling and heating energy demand under climate change C. Tian et al. 10.1016/j.rser.2021.110799
- What Role Does Hydrological Science Play in the Age of Machine Learning? G. Nearing et al. 10.1029/2020WR028091
- Input dropout in product unit neural networks for stream water temperature modelling A. Piotrowski et al. 10.1016/j.jhydrol.2021.126253
- Advances in the Remote Sensing of Terrestrial Evaporation M. McCabe et al. 10.3390/rs11091138
- Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization J. Zhang et al. 10.1029/2020WR027399
- Challenges and opportunities in precision irrigation decision-support systems for center pivots J. Zhang et al. 10.1088/1748-9326/abe436
- Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling S. Razavi 10.1016/j.envsoft.2021.105159
- Process‐Guided Deep Learning Predictions of Lake Water Temperature J. Read et al. 10.1029/2019WR024922
- Machine learning based identification of dominant controls on runoff dynamics H. Oppel & A. Schumann 10.1002/hyp.13740
- Revealing Causal Controls of Storage-Streamflow Relationships With a Data-Centric Bayesian Framework Combining Machine Learning and Process-Based Modeling W. Tsai et al. 10.3389/frwa.2020.583000
- Editorial: Broadening the Use of Machine Learning in Hydrology C. Shen et al. 10.3389/frwa.2021.681023
- Machine Learning Predicts Reach‐Scale Channel Types From Coarse‐Scale Geospatial Data in a Large River Basin H. Guillon et al. 10.1029/2019WR026691
- A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk M. Rohde et al. 10.3389/feart.2021.784499
- Prediction of flood quantiles at ungauged catchments for the contiguous USA using Artificial Neural Networks V. Filipova et al. 10.2166/nh.2021.082
- GRQA: Global River Water Quality Archive H. Virro et al. 10.5194/essd-13-5483-2021
- On doing hydrology with dragons: Realizing the value of perceptual models and knowledge accumulation T. Wagener et al. 10.1002/wat2.1550
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Discussed (final revised paper)
Latest update: 25 Jun 2022
Short summary
Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and scientific disciplines. We argue that DL can offer a complementary avenue toward advancing hydrology. New methods are being developed to interpret the knowledge learned by deep networks. We argue that open competitions, integrating DL and process-based models, more data sharing, data collection from citizen scientists, and improved education will be needed to incubate advances in hydrology.
Recently, deep learning (DL) has emerged as a revolutionary tool for transforming industries and...
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