Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

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

Domain:

climateagriculture

Record type:

paper
Creator:
JerMohIbi
Publisher:
Lea
Host:
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.

Visit

doi.org

Similar

Artificial intelligence models for prediction of monthly rainfall without climatic data for meteorological stations in EthiopiaResults for random forest prediction.Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest ApproachMachine learning models for rainfall prediction over arid climatic regions of South AfricaRandom Forest Prediction Algorithm for Eastern Africa, 1991-2023.Comparative Analysis of Drought Indices for Different Climatic Zones in Benin

Artificial intelligence models for prediction of monthly rainfall without climatic data for meteorological stations in Ethiopia

Abstract Global climate change is affecting water resources and other aspects of life in many count

Results for random forest prediction.

A random forest model based on taxon discrimination by monthly rainfall (Jan-Dec) was used to pre

Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest Approach

With the explosive growth in the world’s population which has little or no corresponding rise in the

Machine learning models for rainfall prediction over arid climatic regions of South Africa

This study focused on predicting rainfall in four arid climatic zones in South Africa using four mac

Random Forest Prediction Algorithm for Eastern Africa, 1991-2023.

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) products are

Comparative Analysis of Drought Indices for Different Climatic Zones in Benin

Abstract The Standardized Precipitation Index (SPI) and the Standardized Precipitation Eva