Monthly rainfall prediction for different climatic zones in south Africa for 2024 using a random forest model

Jeremiah Ayodele Ogunniyi (), Mohamed A. M. Abd Elbasit () and Ibidun Christiana Obagbuwa ()

Edelweiss Applied Science and Technology, 2024, vol. 8, issue 6, 1805-1827

Abstract: This study predicted 2024 rainfall in different climatic zones in South Africa using a random forest model. Previous studies have shown that random forests performed better than other models for rainfall prediction. South Africa was divided into nine using the Koppen-Geiger climate classification system, and three cities were selected for each climatic zone. Atmospheric datasets from the South African Weather Service and the National Aeronautics and Space Agency were used for this study. The datasets were trained, tested, and validated to assess the model's accuracy. With good forecast ability observed, the random forest was then used for monthly rainfall for 2024. The result of this prediction was then compared with 2022 and 2023 rainfall. The result indicated months where much rain should be expected in various cities and cities that may likely experience droughts. This result is particularly important for agriculture, water resource management, and early drought/flooding warning systems.

Keywords: Climate zones; Machine learning; Rainfall prediction; Random forest. (search for similar items in EconPapers)
Date: 2024
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