# NeuralHydrology -- Interpreting LSTMs in Hydrology

arXiv: https://arxiv.org/abs/1903.07903 · HTML: https://mlsanity.com/paper/1903.07903/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2019.

## Built on by (most cited first, 12 of 12)

- [Toward Explainable AI for Regression Models](https://mlsanity.com/paper/2112.11407/) (2021)
- [Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces](https://mlsanity.com/paper/2212.14855/) (2022)
- [Explainable AI for Time Series via Virtual Inspection Layers](https://mlsanity.com/paper/2303.06365/) (2023)
- [MC-LSTM: Mass-Conserving LSTM](https://mlsanity.com/paper/2101.05186/) (2021)
- [Few-Shot Learning by Dimensionality Reduction in Gradient Space](https://mlsanity.com/paper/2206.03483/) (2022)
- [Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources](https://mlsanity.com/paper/2308.09766/) (2023)
- [Transfer learning to improve streamflow forecasts in data sparse regions](https://mlsanity.com/paper/2112.03088/) (2021)
- [Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping](https://mlsanity.com/paper/2305.00640/) (2023)
- [Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Series](https://mlsanity.com/paper/2410.15218/) (2024)
- [Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM](https://mlsanity.com/paper/2106.07963/) (2021)
- [Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic](https://mlsanity.com/paper/2211.11576/) (2022)
- [Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee](https://mlsanity.com/paper/2405.10375/) (2024)

## Abstract (arXiv)

Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations. LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system. On basis of two different catchments, one with snow influence and one without, we demonstrate how the trained model can be analyzed and interpreted. In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system.