Bridging Learning and Forecasting: An Analytical Study of LSTM and CNN Machine Learning Techniques for Weather Prediction

Abstract

The research paper presents a detailed account of how deep learning, more specifically Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures, has been pivotal in advancing weather prediction accuracy as well as reliability. Even though numerical weather prediction models based on traditional physics are generally reliable, they face problems of high computational costs and are not easily adaptable to the nonlinear nature of atmospheric dynamics. LSTM networks are capable of capturing the temporal dependencies in consecutive meteorological data, whereas CNNs can extract the spatial correlations from the satellite imagery and radar data. The study systematically analyses existing research publications (2015–2025) to understand the performance, advantages, and issues of LSTM, CNN, and hybrid CNN–LSTM models. Results show that hybrid structures have better performance than single models for the prediction of temperature, precipitation, wind speed, and extreme weather events as they can more effectively combine spatial-temporal learning. Nevertheless, there are obstacles such as the lack of data, interpretability, and computational demand that still remain. The study outlines potential directions for research in the field of explainable AI, transfer learning, and sustainable computing, which aim to close the gap between data-driven learning and the forecasting of the real world.