Hybrid LSTM-CNN Model with Temporal Feature Engineering and Genetic Algorithm Optimization for Temperature Forecasting

The accurate temperature forecasting system provides essential benefits for managing outdoor activities, controlling electricity consumption, and ensuring public health and safety in areas with extreme heat. The researchers developed a hybrid Long Short-Term Memory–Convolutional Neural Network (LSTM–CNN) model that uses daily time-series data from Makkah, Saudi Arabia, to enhance short-term temperature prediction results. The forecasting task is defined as daily multi-step prediction, generating 1-day, 3-day, and 6-day ahead temperature forecasts. The proposed model combines LSTM networks to capture long-term temporal dependencies and CNN to extract short-term variations. The system uses temporal features, lag features, and rolling statistical features to improve data representation, while Genetic Algorithm (GA) optimization handles the selection of model hyperparameters. The framework uses ten-fold cross-validation to test its performance, ensuring consistent performance across all testing scenarios. The results demonstrate strong predictive accuracy, with the GA-optimized model achieving a Mean Absolute Error (MAE) of 0.55 °C for 1-day forecasts and 1.28 °C for 6-day forecasts, with R2 values reaching up to 0.98. The proposed model outperformed Autoregressive Integrated Moving Average (ARIMA), LSTM, and Transformer models during benchmark tests, providing better forecasting results across various time intervals. These findings indicate that the proposed model demonstrates accurate and reliable temperature forecasting performance for arid to semi-arid climatic conditions.