Summary of Implementing a Gru Neural Network For Flood Prediction in Ashland City, Tennessee, by George K. Fordjour and Alfred J. Kalyanapu
Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee
by George K. Fordjour, Alfred J. Kalyanapu
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a robust flood prediction model for Ashland City, Tennessee, utilizing water level data from USGS gauge stations. A Gated Recurrent Unit (GRU) network is used to process sequential time-series data. The model is trained, validated, and tested using a year-long dataset from January 2021 to January 2022. Performance metrics include Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Percent Bias (PBIAS), Mean Absolute Error (MAE), and Coefficient of Determination (R^2). The results show high accuracy, with the model explaining 98.2% of the variance in data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps predict floods in Ashland City by using special computer models. It uses water level data from nearby sensors to train a “memory” computer that can learn patterns over time. The model is tested on a year’s worth of data and does a great job predicting future flood levels. This could help people prepare for and respond to flooding disasters. |
Keywords
» Artificial intelligence » Mae » Time series
