Melckykaisha/Weather_Forcust_Kenya_DL_Models

Deep learning system for hyper-local weather forecasting across all 47 Kenya counties. Trains and compares LSTM, GRU & ConvLSTM models on 10 years of historical meteorological data. Interactive 7-day forecast map built with Streamlit & Folium. # 🌦️ Kenya Local Weather Forecasting System A deep learning-based weather forecasting system that predicts local weather conditions across all **47 counties in Kenya** using historical meteorological data. Built with LSTM, GRU, and ConvLSTM neural networks and deployed as an interactive Streamlit web application. --- ## 📌 Overview Kenya's diverse geography — from coastal plains to central highlands and arid semi-arid lands (ASALs) — creates complex microclimates that are poorly served by broad national forecasts. This system addresses that gap by training deep learning models on 10 years of historical weather data to generate **county-level, 7-day forecasts** for temperature, rainfall, humidity, wind speed, and atmospheric pressure. --- ## 🚀 Live Demo > **Launch App →** --- ## 📸 App Pages | Page | Description | |------|-------------| | 🗺️ **Kenya Map** | Interactive map with all 47 county markers. Click any county for a 7-day forecast popup | | 📍 **County Forecast** | Select any county to view detailed daily predictions with trend charts | | 📊 **Model Comparison** | Side-by-side evaluation of LSTM, GRU, and ConvLSTM using MAE, RMSE, and R² | | 📈 **Predicted vs Actual** | Scatter and time-series plots validating model accuracy against real observations | --- ## 🧠 Models Three deep learning architectures were trained and compared: | Model | Description | |-------|-------------| | **LSTM** | Long Short-Term Memory — captures long-range temporal dependencies | | **GRU** | Gated Recurrent Unit — faster, lighter alternative to LSTM | | **ConvLSTM** | Convolutional LSTM — combines spatial feature extraction with temporal modeling | **Best performing model: GRU** (lowest RMSE across all variables) --- ## 📊 Predicted Variables | Variable | Unit | |----------|------| | Mean Temperature | °C | | Rainfall | mm | | Relative Humidity | % | | Wind Speed | km/h | | Atmospheric Pressure | hPa | - **Input window:** 30 days of historical data - **Forecast horizon:** …