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A Comparative Study of Long Short-Term Memory and Gated Recurrent Units for Forecasting Rainfall: A Case Study of Nigeria

by Oguche Ajah, Abimbola H. Afolayan, Akintoba E. Akinwonmi
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 46
Year of Publication: 2025
Authors: Oguche Ajah, Abimbola H. Afolayan, Akintoba E. Akinwonmi
10.5120/ijais2025451987

Oguche Ajah, Abimbola H. Afolayan, Akintoba E. Akinwonmi . A Comparative Study of Long Short-Term Memory and Gated Recurrent Units for Forecasting Rainfall: A Case Study of Nigeria. International Journal of Applied Information Systems. 12, 46 ( Jan 2025), 15-24. DOI=10.5120/ijais2025451987

@article{ 10.5120/ijais2025451987,
author = { Oguche Ajah, Abimbola H. Afolayan, Akintoba E. Akinwonmi },
title = { A Comparative Study of Long Short-Term Memory and Gated Recurrent Units for Forecasting Rainfall: A Case Study of Nigeria },
journal = { International Journal of Applied Information Systems },
issue_date = { Jan 2025 },
volume = { 12 },
number = { 46 },
month = { Jan },
year = { 2025 },
issn = { 2249-0868 },
pages = { 15-24 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number46/a-comparative-study-of-long-short-term-memory-and-gated-recurrent-units-for-forecasting-rainfall-a-case-study-of-nigeria/ },
doi = { 10.5120/ijais2025451987 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-02-01T00:51:39.752176+05:30
%A Oguche Ajah
%A Abimbola H. Afolayan
%A Akintoba E. Akinwonmi
%T A Comparative Study of Long Short-Term Memory and Gated Recurrent Units for Forecasting Rainfall: A Case Study of Nigeria
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 46
%P 15-24
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Heavy rainfall may trigger floods, destroying human life and the agricultural economy. Accurate rainfall forecasting is vital for mitigating the effects of severe flooding. This study compares deep supervised learning (DSL) algorithms for rainfall forecasting in Nigeria, using a large dataset from ECMWF archives that includes Kaduna, Bauchi, Kwara, Imo, Ondo, and Cross River states representing the six geopolitical zones in Nigeria. Critical attributes in the dataset include rainfall, relative humidity, temperature, dew point, surface pressure, and wind speed. Following meticulous preprocessing and outlier removal, feature engineering was done using Pearson correlation and Spearman rank to ensure the most important predictors were chosen. Two sophisticated models— Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were developed, trained, and tested extensively. The models were assessed using a variety of metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared, and Accuracy. The findings were impressive, with the GRU model surpassing the LSTM model with the average MSE, MAE, R-Square, RMSE, and Accuracy values of 3.0504, 1.1644, 0.7978, 1.7381, and 98.43%, respectively. These findings illustrate GRU's exceptional capacity to capture complicated patterns of rainfall data, making it an effective tool for meteorological forecasting.

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Index Terms

Computer Science
Information Sciences

Keywords

Rainfall forecasting in Nigeria Deep Supervised Learning Algorithms Long Short-Term Memory Gated Recurrent Unit Evaluation Metrics

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