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10 papers analyzed
These studies suggest that machine learning techniques, including Extreme Gradient Boosting, deep learning models, classification techniques, and the Bayesian Processor of Output method, can accurately predict rainfall in Western Africa, benefiting agriculture, weather forecasting, and flood prevention.
20 papers analyzed
Accurate rainfall prediction in Western Africa is crucial for agricultural planning, water resource management, and mitigating the impacts of floods and droughts. Machine learning (ML) techniques have shown promise in improving the accuracy of rainfall forecasts by leveraging historical weather data and identifying complex patterns.
Machine Learning Models for Rainfall Prediction:
Deep Learning Techniques:
Statistical and Hybrid Approaches:
Challenges and Considerations:
Machine learning and deep learning techniques offer promising tools for improving rainfall prediction in Western Africa. Models such as Extreme Gradient Boosting, CNNs, ConvLSTM, and hybrid approaches like SVR-RF have demonstrated effectiveness in various studies. However, challenges such as overfitting and the irregular nature of rainfall patterns necessitate careful model selection and validation. Overall, integrating advanced ML techniques with traditional methods can enhance the accuracy and reliability of rainfall forecasts in the region.
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