Hybrid CNN–LSTM Models for Weather Forecasting
TL;DR
The theory, architecture, and performance of CNN-LSTM models are explored, where CNNs extract spatial features from meteorological data and LSTMs capture temporal dependencies for sequential forecasting across multiple time horizons.
Abstract
Accurate weather prediction is crucial for sectors such as agriculture, disaster management, transportation, and energy. Traditional numerical weather prediction (NWP) models rely on complex physical equations and high computational resources, but often struggle with localized patterns and nonlinear spatio-temporal relationships. Recently, machine learning approaches, particularly hybrid CNN-LSTM models, have emerged as effective alternatives by combining spatial and temporal learning capabilities. This paper explores the theory, architecture, and performance of CNN-LSTM models, where CNNs extract spatial features from meteorological data and LSTMs capture temporal dependencies for sequential forecasting. The study reviews existing methods, including statistical, NWP, and deep learning models such as CNNs, RNNs, GRUs, and attention mechanisms. The proposed methodology includes data preprocessing, feature normalization, spatial encoding, temporal modeling, and supervised multi-step prediction. Performance is evaluated using MAE, RMSE, and R² metrics. Results show that hybrid CNN-LSTM models outperform baseline methods in accuracy, robustness, and forecasting across multiple time horizons. The paper concludes with future directions, including explainable AI, physics-informed models, and integration with NWP systems.