Weather Forecasting Using LSTM Neural Networks, Temperature Prediction - Seyed Ebrahim Hosseini; Shahbaz Pervez; Kazim Raza Talpur; Omar Alhawi; Abdul Salam Shah; Asadullah Shah (Journal of ICT, Design, Engineering and Technological Science, 2025)
The study explores the field of weather prediction with attention to its real‑world use and the quality that can be enhanced with the help of the most advanced methods of machine learning. It particularly studies the application of long short‑ term memory (LSTM) networks to improve forecasting. The paper relies on the data collected in Delhi, India, to train and evaluate the LSTM model. The paper identifies critical gaps in weather forecasting studies and investigates the effects of the gaps on business and lives. Accurate weather prediction can be used in such industries as agriculture, transport and disaster management, where a small development may have tremendous consequences. The paper provides a clear overview of LSTM model, the architecture, validation policy and evaluation. LSTM networks are the most appropriate networks to make the weather forecasts given that they are capable of repeating the patterns using the time series data. The paper will reveal that LSTM networks are efficient to improve the accuracy of weather prediction as well as how they may revolutionize industries that are reliant on the accuracy of predictions. This model was trained in this study based on an effective evaluation of MSE 0.034.