simoghost99/DeepCAPE-Convective-Available-Potential-Energy-Prediction-using-Convolutional-Neural-Networks

DeepCAPE applies CNN to predict Convective Available Potential Energy using ERA5 meteorological data over Morocco. Complete pipeline from NetCDF processing to model evaluation with temporal validation strategies. # DeepCAPE-Convective-Available-Potential-Energy-Prediction-using-Convolutional-Neural-Networks DeepCAPE applies CNN to predict Convective Available Potential Energy using ERA5 meteorological data over Morocco. Complete pipeline from NetCDF processing to model evaluation with temporal validation strategies. # Key Features Data Pipeline: Processes NetCDF meteorological data using Xarray library CNN Architecture: Implements 1D convolutional neural networks for regression Temporal Validation: Uses mixed-date splitting strategy to avoid seasonal bias Comprehensive Evaluation: Includes cross-validation, hyperparameter tuning, and seasonal performance analysis Operational Focus: Designed with meteorological forecasting applications in mind # Technical Stack Data Processing: Xarray, Pandas, NumPy Machine Learning: TensorFlow/Keras, scikit-learn Visualization: Matplotlib, Seaborn Data Format: NetCDF, CSV # Project Structure The repository contains implementations of all project phases: Data preprocessing and conversion from NetCDF to DataFrame Exploratory data analysis and feature engineering CNN model development with hyperparameter tuning Cross-validation and performance evaluation Seasonal and temporal analysis of predictions # Results Summary The model achieved: R² score of 0.5974 on test data Mean Absolute Error of 37.27 J/kg Significant improvement over traditional temporal splitting approaches Best performance during summer conditions