# A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

arXiv: https://arxiv.org/abs/2007.12269 · HTML: https://mlsanity.com/paper/2007.12269/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 30 of 30)

- [Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience](https://mlsanity.com/paper/2202.03407/) (2022)
- [Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset](https://mlsanity.com/paper/2103.10005/) (2021)
- [Short-term Hourly Streamflow Prediction with Graph Convolutional GRU Networks](https://mlsanity.com/paper/2107.07039/) (2021)
- [The Merit of River Network Topology for Neural Flood Forecasting](https://mlsanity.com/paper/2405.19836/) (2024)
- [Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology](https://mlsanity.com/paper/2310.05227/) (2023)
- [The data synergy effects of time-series deep learning models in hydrology](https://mlsanity.com/paper/2101.01876/) (2021)
- [IowaRain: A Statewide Rain Event Dataset Based on Weather Radars and Quantitative Precipitation Estimation](https://mlsanity.com/paper/2107.03432/) (2021)
- [A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models](https://mlsanity.com/paper/2206.08523/) (2022)
- [DEM Super-Resolution with EfficientNetV2](https://mlsanity.com/paper/2109.09661/) (2021)
- [Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks](https://mlsanity.com/paper/2205.03508/) (2022)
- [Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications](https://mlsanity.com/paper/2210.08347/) (2022)
- [High-resolution rainfall-runoff modeling using graph neural network](https://mlsanity.com/paper/2110.10833/) (2021)
- [Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry](https://mlsanity.com/paper/2111.11719/) (2021)
- [Earthquake Nowcasting with Deep Learning](https://mlsanity.com/paper/2201.01869/) (2021)
- [EfficientTempNet: Temporal Super-Resolution of Radar Rainfall](https://mlsanity.com/paper/2303.05552/) (2023)
- [Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies](https://mlsanity.com/paper/2401.10266/) (2024)
- [Opportunistic Emulation of Computationally Expensive Simulations via Deep Learning](https://mlsanity.com/paper/2108.11057/) (2021)
- [TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs](https://mlsanity.com/paper/2109.09289/) (2021)
- [Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data](https://mlsanity.com/paper/2110.00841/) (2021)
- [A Neural Emulator for Uncertainty Estimation of Fire Propagation](https://mlsanity.com/paper/2305.06139/) (2023)
- [Deep Learning Hydrodynamic Forecasting for Flooded Region Assessment in Near-Real-Time (DL Hydro-FRAN)](https://mlsanity.com/paper/2305.12052/) (2023)
- [An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone](https://mlsanity.com/paper/2309.07992/) (2023)
- [Deep-Wide Learning Assistance for Insect Pest Classification](https://mlsanity.com/paper/2409.10445/) (2024)
- [HydroGAT: Distributed Heterogeneous Graph Attention Transformer for Spatiotemporal Flood Prediction](https://mlsanity.com/paper/2509.02481/) (2025)
- [SWAT Watershed Model Calibration using Deep Learning](https://mlsanity.com/paper/2110.03097/) (2021)
- [Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia](https://mlsanity.com/paper/2112.12966/) (2021)
- [Ensemble random forest filter: An alternative to the ensemble Kalman filter for inverse modeling](https://mlsanity.com/paper/2207.03909/) (2022)
- [A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant](https://mlsanity.com/paper/2401.11217/) (2024)
- [FPGA Deployment of LFADS for Real-time Neuroscience Experiments](https://mlsanity.com/paper/2402.04274/) (2024)
- [Machine Learning for Proactive Groundwater Management: Early Warning and Resource Allocation](https://mlsanity.com/paper/2506.22461/) (2025)

## Abstract (arXiv)

The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety, and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research which incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.