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Title: A Dynamic Hybrid Weather Forecasting Model for Olive Tree Cultivation in Mediterranean Climates
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Goal: Forecast daily weather in olive-producing regions using machine learning and uncertainty-aware methods
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Approach: Combined Gated Recurrent Units with Bayesian Ensemble Kalman Filters for dynamic updates
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Data: 5 Mediterranean cities (Cyprus, Turkey, Greece, Spain, Italy)
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Tools: Python, TensorFlow, Prophet, Kalman Filters
High-Level Steps Overview:
Given a prior belief (my initial model weights) and a new observation (real-time temperature data), how should the model revise it's belief(update its weights) ?
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