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

Abstract

As 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 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 evaluated using Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The findings demonstrate that, in comparison to other algorithms, Extreme Gradient Boost regression has the lowest MAE values of 0.006, 0.018, 0.005 for Lake Victoria basin weather data in Uganda, Kenya, and Tanzania respectively.

Keywords

• Precipitation amount
• weather prediction
• data-driven approaches
• short-term forecasting