mlsanityData to 7 Oct 2026

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

arXiv 1712.02162 ↗First posted 6 Dec 2017stat.ML cs.LGcited by 47

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Abstract (arXiv)

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 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.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 11; 2022: 8; 2023: 7; 2024: 9; 2025: 10; 2026: 2.

47 papers.

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  1. 2022Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geosciencecited by 28
  2. 2022Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracycited by 21
  3. 2021Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Datasetcited by 18
  4. 2022A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecastingcited by 12
  5. 2021The data synergy effects of time-series deep learning models in hydrologycited by 10
  6. 2022Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solverscited by 6
  7. 2023Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resourcescited by 6
  8. 2023A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systemscited by 6
  9. 2022Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networkscited by 5
  10. 2021Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategycited by 4
  11. 2021Transfer learning to improve streamflow forecasts in data sparse regionscited by 4
  12. 2023Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mappingcited by 4
35 more
  1. 2023Differentiable modeling to unify machine learning and physical models and advance Geosciencescited by 3
  2. 2024Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Seriescited by 3
  3. 2024Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamicscited by 3
  4. 2021Improving prediction of the terrestrial water storage anomalies during the GRACE and GRACE-FO gap with Bayesian convolutional neural networkscited by 2
  5. 2021Surrogate Model for Shallow Water Equations Solvers with Deep Learningcited by 2
  6. 2022Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Managementcited by 2
  7. 2024MLP-KAN: Unifying Deep Representation and Function Learningcited by 2
  8. 2024Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometriescited by 2
  9. 2025Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Frameworkcited by 2
  10. 2021Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTMcited by 1
  11. 2023Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusioncited by 1
  12. 2024Applications of Explainable artificial intelligence in Earth system sciencecited by 1
  13. 2024Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecastingcited by 1
  14. 2024Spatio-temporal Causal Learning for Streamflow Forecastingcited by 1
  15. 2021Machine Learning for Postprocessing Ensemble Streamflow Forecastscited by 0
  16. 2021SWAT Watershed Model Calibration using Deep Learningcited by 0
  17. 2021Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modelingcited by 0
  18. 2021Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asiacited by 0
  19. 2022Application of Machine Learning Methods in Inferring Surface Water Groundwater Exchanges using High Temporal Resolution Temperature Measurementscited by 0
  20. 2022Towards Daily High-resolution Inundation Observations using Deep Learning and EOcited by 0
  21. 2023Efficient machine-learning surrogates for large-scale geological carbon and energy storagecited by 0
  22. 2023Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognitioncited by 0
  23. 2024Methods to improve run time of hydrologic models: opportunities and challenges in the machine learning eracited by 0
  24. 2024Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Prediction using Weather Image Time Seriescited by 0
  25. 2025Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic modelscited by 0
  26. 2025Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Predictioncited by 0
  27. 2025Machine Learning for Proactive Groundwater Management: Early Warning and Resource Allocationcited by 0
  28. 2025Deep Learning with Self-Attention and Enhanced Preprocessing for Precise Diagnosis of Acute Lymphoblastic Leukemia from Bone Marrow Smears in Hemato-Oncologycited by 0
  29. 2025Explainable artificial intelligence (XAI) for scaling: An application for deducing hydrologic connectivity at watershed scalecited by 0
  30. 2025Physics-guided Emulators Reveal Resilience and Fragility under Operational Latencies and Outagescited by 0
  31. 2025Opinion Mining and Analysis Using Hybrid Deep Neural Networkscited by 0
  32. 2025TabGRU: An Enhanced Design for Urban Rainfall Intensity Estimation Using Commercial Microwave Linkscited by 0
  33. 2025CauSTream: Causal Spatio-Temporal Representation Learning for Streamflow Forecastingcited by 0
  34. 2026Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraintscited by 0
  35. 2026Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systemscited by 0