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

arXiv: https://arxiv.org/abs/1712.02162 · HTML: https://mlsanity.com/paper/1712.02162/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2017.

## Built on by (most cited first, 30 of 47)

- [Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience](https://mlsanity.com/paper/2202.03407/) (2022)
- [Differentiable, learnable, regionalized process-based models with physical outputs can approach state-of-the-art hydrologic prediction accuracy](https://mlsanity.com/paper/2203.14827/) (2022)
- [Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset](https://mlsanity.com/paper/2103.10005/) (2021)
- [A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting](https://mlsanity.com/paper/2206.08998/) (2022)
- [The data synergy effects of time-series deep learning models in hydrology](https://mlsanity.com/paper/2101.01876/) (2021)
- [A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems](https://mlsanity.com/paper/2310.08644/) (2023)
- [Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers](https://mlsanity.com/paper/2203.02821/) (2022)
- [Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources](https://mlsanity.com/paper/2308.09766/) (2023)
- [Inferring electrochemical performance and parameters of Li-ion batteries based on deep operator networks](https://mlsanity.com/paper/2205.03508/) (2022)
- [Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy](https://mlsanity.com/paper/2101.04423/) (2021)
- [Transfer learning to improve streamflow forecasts in data sparse regions](https://mlsanity.com/paper/2112.03088/) (2021)
- [Differentiable modeling to unify machine learning and physical models and advance Geosciences](https://mlsanity.com/paper/2301.04027/) (2023)
- [Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping](https://mlsanity.com/paper/2305.00640/) (2023)
- [Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics](https://mlsanity.com/paper/2412.04845/) (2024)
- [Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Series](https://mlsanity.com/paper/2410.15218/) (2024)
- [Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries](https://mlsanity.com/paper/2501.01453/) (2024)
- [Improving prediction of the terrestrial water storage anomalies during the GRACE and GRACE-FO gap with Bayesian convolutional neural networks](https://mlsanity.com/paper/2101.09361/) (2021)
- [Surrogate Model for Shallow Water Equations Solvers with Deep Learning](https://mlsanity.com/paper/2112.10889/) (2021)
- [Forecasting Soil Moisture Using Domain Inspired Temporal Graph Convolution Neural Networks To Guide Sustainable Crop Management](https://mlsanity.com/paper/2212.06565/) (2022)
- [MLP-KAN: Unifying Deep Representation and Function Learning](https://mlsanity.com/paper/2410.03027/) (2024)
- [Learning to Retrieve for Environmental Knowledge Discovery: An Augmentation-Adaptive Self-Supervised Learning Framework](https://mlsanity.com/paper/2509.14563/) (2025)
- [Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM](https://mlsanity.com/paper/2106.07963/) (2021)
- [Improving Urban Flood Prediction using LSTM-DeepLabv3+ and Bayesian Optimization with Spatiotemporal feature fusion](https://mlsanity.com/paper/2304.09994/) (2023)
- [Applications of Explainable artificial intelligence in Earth system science](https://mlsanity.com/paper/2406.11882/) (2024)
- [Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting](https://mlsanity.com/paper/2410.05536/) (2024)
- [Spatio-temporal Causal Learning for Streamflow Forecasting](https://mlsanity.com/paper/2411.17937/) (2024)
- [Machine Learning for Postprocessing Ensemble Streamflow Forecasts](https://mlsanity.com/paper/2106.09547/) (2021)
- [SWAT Watershed Model Calibration using Deep Learning](https://mlsanity.com/paper/2110.03097/) (2021)
- [Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling](https://mlsanity.com/paper/2111.04732/) (2021)
- [Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia](https://mlsanity.com/paper/2112.12966/) (2021)

## 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.