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AI, Physics, and Hybrid Models for Rainfall Prediction in Katsina
Benchmarks AI, physics-based, and hybrid models for daily rainfall prediction in semi-arid Katsina using ERA5 data.
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Comparative Evaluation of AI, Physics, and Hybrid Models for Daily Rainfall Prediction in Semi-Arid Katsina, Nigeria
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Nest Africa AI
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Featured Publication
Comparative Evaluation of AI, Physics, and Hybrid Models for Daily Rainfall Prediction in Semi-Arid Katsina, Nigeria
This study compares AI, physics-based, and hybrid modeling approaches for daily rainfall prediction in semi-arid Katsina, Nigeria. The hybrid model delivered the strongest results, combining physical interpretability with machine-learning flexibility for data-limited climates.
Research Highlights
- Hybrid model achieved RMSE = 1.88 mm, R2 = 0.77, and KGE = 0.78.
- ERA5 daily reanalysis data supported model development for 2014-2024.
- Results support operational early warning for agriculture, water planning, and disaster preparedness.
Recent Publications
Earth Observation
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