Proceedings of the 8th URSI-NG Annual Conference (URSI-NG 2024)

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Grid-Search Criterion Technique for ARMA Model Optimization for Rain Rate Prediction in a Tropical Location

Authors
D. B. Akoma1, *, S. D. Adeyeye1, O. Opeyemi2, O. T. Ayoola3, M. Gbalaja4, J. S. Ojo1
1Federal University of Technology, Akure, Nigeria
2Bamidele Olumilua University of Education Science and Technology Ikere, Ikere, Ekiti, Nigeria
3Ekiti State University, Ado-Ekiti, Nigeria
4Federal College of Education, Okene, Kogi State, Nigeria
*Corresponding author. Email: dbakoma@futa.edu.ng Email: akoma.blessing.d@gmail.com
Corresponding Author
D. B. Akoma
Available Online 4 February 2025.
DOI
10.2991/978-94-6463-644-4_25How to use a DOI?
Keywords
MRR; ARMA; Grid-search criterion; Rain rate
Abstract

Intense rainfall is a significant factor affecting communication systems as radio signals interact with raindrops along their propagation path in the troposphere. However, the spatial and temporal variability of rainfall makes its measurement and prediction challenging. This study endeavors to investigate statistical methodologies for predicting time series values of rain rate using rainfall data collected for seven months in the year 2014's rainy season. The collection was gathered by the Communication Physics Research Group using the Micro Rain Radar installed within the Department of Physics. The study focuses on understanding rain rate patterns across various heights, with particular attention to the 320m height due to the susceptibility of the 160m height to near-field effects. After outlier removal, statistical distributions and monthly trends were analyzed, revealing distinct peaks and clustering of rain occurrences post-August. The First Order, regressive model, auto regressive model and Auto Regressive Moving Average (ARMA)models has been used in predicting time series values of rain rate. Ultimately, the ARMA model emerged as the most effective, leveraging both autoregressive and moving average components to enhance predictive accuracy by capturing complex data dependencies.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 8th URSI-NG Annual Conference (URSI-NG 2024)
Series
Advances in Physics Research
Publication Date
4 February 2025
ISBN
978-94-6463-644-4
ISSN
2352-541X
DOI
10.2991/978-94-6463-644-4_25How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - D. B. Akoma
AU  - S. D. Adeyeye
AU  - O. Opeyemi
AU  - O. T. Ayoola
AU  - M. Gbalaja
AU  - J. S. Ojo
PY  - 2025
DA  - 2025/02/04
TI  - Grid-Search Criterion Technique for ARMA Model Optimization for Rain Rate Prediction in a Tropical Location
BT  - Proceedings of the 8th URSI-NG Annual Conference (URSI-NG 2024)
PB  - Atlantis Press
SP  - 253
EP  - 263
SN  - 2352-541X
UR  - https://doi.org/10.2991/978-94-6463-644-4_25
DO  - 10.2991/978-94-6463-644-4_25
ID  - Akoma2025
ER  -