Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana

Simple item page
dc.contributor.authorAppiah-Badu, Nana Kofi Ahoi
dc.contributor.authorMissah, Yaw Marfo
dc.contributor.authorAmekudzi, Leonard K.
dc.contributor.authorNajim, Ussiph
dc.contributor.authorTwum, Frimpong
dc.contributor.authorAhene, Emmanuell
dc.contributor.orcid0000-0002-3029-4498
dc.contributor.orcid0000-0002-2186-3425
dc.contributor.orcid0000-0002-2926-681X
dc.contributor.orcid0000-0002-6973-7495
dc.contributor.orcid0000-0002-1869-7542
dc.contributor.orcid0000-0002-0810-1055
dc.date.accessioned2023-12-06T14:58:30Z
dc.date.available2023-12-06T14:58:30Z
dc.date.issued2022
dc.descriptionAn article published in IEEE Access, Vol. 10, 2022; Digital Object Identifier 10.1109/ACCESS.2021.3139312
dc.description.abstractAccurate rainfall prediction has become very complicated in recent times due to climate change and variability. The efficiency of classification algorithms in rainfall prediction has flourished. The study contributes to using various classification algorithms for rainfall prediction in the different ecological zones of Ghana. The classification algorithms include Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB) and K-Nearest Neighbour (KNN). The dataset, consisting of various climatic attributes, was sourced from the Ghana Meteorological Agency spanning 1980 – 2019. The performance of the classification algorithms was examined based on precision, recall, f1-score, accuracy and execution time with various training and testing data ratios. On all three training and testing ratios: 70:30, 80:20 and 90:10, RF, XGB and MLP performed well, whereas KNN performed least across all zones. In terms of the execution time of the models, Decision Tree is consistently portrayed as the fastest, whereas MLP used the most run time.
dc.description.sponsorshipKNUST
dc.identifier.citationIEEE Access, Vol. 10, 2022; Digital Object Identifier 10.1109/ACCESS.2021.3139312
dc.identifier.uriDigital Object Identifier 10.1109/ACCESS.2021.3139312
dc.identifier.urihttps://ir.knust.edu.gh/handle/123456789/14678
dc.language.isoen
dc.publisherIEEE Access
dc.titleRainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Rainfall_Prediction_Using_Machine_Learning_Algorithms_for_the_Various_Ecological_Zones_of_Ghana.pdf
Size:
2.61 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Articles