# Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee

arXiv: https://arxiv.org/abs/2405.10375 · HTML: https://mlsanity.com/paper/2405.10375/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 9 of 9)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks](https://mlsanity.com/paper/1701.05923/) (2017)
- [Flood Prediction Using Machine Learning Models: Literature Review](https://mlsanity.com/paper/1908.02781/) (2019)
- [NeuralHydrology -- Interpreting LSTMs in Hydrology](https://mlsanity.com/paper/1903.07903/) (2019)
- [Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification](https://mlsanity.com/paper/2211.06463/) (2022)
- [Deep Gate Recurrent Neural Network](https://mlsanity.com/paper/1604.02910/) (2016)
- [Comparative Analysis of Machine Learning Models for Predicting Travel Time](https://mlsanity.com/paper/2111.08226/) (2021)
- [Decentralized Flood Forecasting Using Deep Neural Networks](https://mlsanity.com/paper/1902.02308/) (2019)

## Abstract (arXiv)

Ashland City, Tennessee, located within the Lower Cumberland Sycamore watershed, is highly susceptible to flooding due to increased upstream water levels. This study aimed to develop a robust flood prediction model for the city, utilizing water level data at 30-minute intervals from ten USGS gauge stations within the watershed. A Gated Recurrent Unit (GRU) network, known for its ability to effectively process sequential time-series data, was used. The model was trained, validated, and tested using a year-long dataset (January 2021-January 2022), and its performance was evaluated using statistical metrics including Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Percent Bias (PBIAS), Mean Absolute Error (MAE), and Coefficient of Determination (R^2). The results demonstrated a high level of accuracy, with the model explaining 98.2% of the variance in the data. Despite minor discrepancies between predicted and observed values, the GRU model proved to be an effective tool for flood prediction in Ashland City, with potential applications for enhancing disaster preparedness and response efforts in Ashland City.