Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataset

Simple item page
dc.contributor.authorGahwera, Tumusiime Andrew
dc.contributor.authorEyobu, Odongo Steven
dc.contributor.authorIsaac, Mugume
dc.date.accessioned2024-05-14T10:01:45Z
dc.date.available2024-05-14T10:01:45Z
dc.date.issued2024-05
dc.description.abstractAs a result of climate change, the difficulty in the prediction of short-term rainfall amounts has become a necessary area of research. The existing numerical weather prediction models have limitations in precipitation forecasting especially due to high computation requirements and are prone to errors. Precipitation amount prediction is challenging as it requires knowledge on a variety of environmental phenomena, such as temperature, humidity, wind direction, and more over a long period of time. In this study, we first of all present our Lake Victoria Basin weather dataset and then use it to conduct a rigorous analysis of machine learning algorithms to do short term rainfall prediction. The rigorous analysis includes algorithm optimizations to improve prediction performance. In particular, we intend to validate our weather dataset using various machine learning regression models which include Random Forest regression, Support vector regression, Neural Network regression, Least Absolute Shrinkage and Selection Operator regression, Gradient boosting regression, and Extreme Gradient boosting regression. The performance of the models was assessed using Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) performance metrics. The findings demonstrate that, in comparison to other algorithms, Extreme Gradient Boost Regression had the lowest MAE values of 0.006, 0.018, 0.005 for Lake Victoria basin weather data in Uganda, Kenya, and Tanzania respectively.en_US
dc.identifier.citationGahwera, Tumusiime Andrew, Odongo Steven Eyobu, and Mugume Isaac. 'Analysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts using Uganda's Lake Victoria Basin Weather Dataset', IEEE Access, (2024), pp. 1-1.en_US
dc.identifier.issnEISSN 2169-3536
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/9525
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectPrecipitation amount, weather prediction, data-driven approaches, short-term forecastingen_US
dc.titleAnalysis of Machine Learning Algorithms for Prediction of Short-Term Rainfall Amounts Using Uganda’s Lake Victoria Basin Weather Dataseten_US
dc.typeArticleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Analysis_of_Machine_Learning_Algorithms_for_Prediction_of_Short-Term_Rainfall_Amounts_Using_Ugandas_Lake_Victoria_Basin_Weather_Dataset.pdf
Size:
2.42 MB
Format:
Adobe Portable Document Format
Description:
Journal Article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Natural Sciences