Paper Title
Evaluating the Suitability of Machine Learning Algorithms for Predicting Extreme Weather Events in Nigeria using Geospatial Data and Climate Variables
Article Identifiers
Authors
Dauda Sulaimon A. , Orimogunje O. O. I
Keywords
Extreme weather events, Machine learning algorithms, Weather prediction, Geospatial data, Climate variables
Abstract
Extreme weather events have become increasingly frequent and severe in recent years, posing significant challenges to societies, economies, and the environment. Accurate prediction of these events is crucial for disaster preparedness and climate resilience. While traditional weather prediction methods have limitations in predicting extreme events accurately, advancements in machine learning (ML) techniques show promise in improving weather forecasting. This study aims to evaluate the suitability of ML algorithms for predicting extreme weather events in Nigeria using geospatial data and climate variables. An extensive evaluation of ML methods will be conducted, and the results will provide valuable insights for disaster management and climate resilience in the region.
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How To Cite (APA)
Dauda Sulaimon A. & Orimogunje O. O. I (August-2023). Evaluating the Suitability of Machine Learning Algorithms for Predicting Extreme Weather Events in Nigeria using Geospatial Data and Climate Variables. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(8), b949-b966. https://ijnrd.org/papers/IJNRD2308206.pdf
Issue
Volume 8 Issue 8, August-2023
Pages : b949-b966
Other Publication Details
Paper Reg. ID: IJNRD_203100
Published Paper Id: IJNRD2308206
Downloads: 000121984
Research Area: Engineering
Country: Ile Ife, Osun State, Nigeria
Published Paper PDF: https://ijnrd.org/papers/IJNRD2308206.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2308206
About Publisher
Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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Licence
This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition
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Current Issue: Volume 10 | Issue 10 | October 2025
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