Towards an automated weather forecasting and classification using deep learning, fully convolutional network, and long short-term memory - Shelke, Nilesh; Maurya, Sudhanshu; Ithape, Rupali; Shaikh, Zarina; Somkunwar, Rachna; Pimpalkar, Amit (International Journal of Electrical and Computer Engineering (IJECE), 2025)
Historically, weather forecasting was unreliable and imprecise, relying on intuition and local knowledge. Inaccurate weather forecasts can cause severe impacts on agriculture, construction, and daily life. Existing methods struggle with rural and urban weather prediction, requiring faster and more accurate solutions. This research proposes a deep learning system using real- time images to address this challenge. This research employs a deep learning model fully convolutional network-long short-term memory (FCN-LSTM) to analyze images and predict weather conditions. In this case, the model forecasts a sunny and cloudy environment, which facilitates defining the ideal conditions for every given climatic zone in the weather classification model. The model is trained on a dataset of weather images obtained from Kaggle. The performance of the proposed model FCN-LSTM achieves an accuracy of 96.88% and a validation accuracy of 91.22%. Also, the mean squared error (MSE) is 7.11, which is significantly lower and supports efficient enhancement in weather forecasting. This significant improvement demonstrates the potential of deep learning for real-time weather forecasting. The model provides efficient weather classification, enabling informed decision-making across various sectors. This research lays the foundation for automated weather analysis using deep learning, eliminating human bias and improving accuracy.