Weather forecasting is a major discipline that plays an important role in fields such as agriculture, transport, and emergency management, and it largely depends on accurate forecasts. Concerning this problem, this work aimed to analyze the effectiveness of recurrent neural networks, particularly the Long Short-Term Memory (LSTM), for estimating rainfall depending on precipitation, maximum temperature, minimum temperature, and wind speed. We will therefore use a large database containing recorded weather data obtained over several years to calibrate accurate predictive models designed to distinguish between drizzle, rain, sun, snow, and fog. The main idea of the work is to teach LSTM models that are capable of revealing temporal relations and patterns in sequential data, which makes them suitable for time-series forecasting such as weather prediction. The data is preprocessed effectively to clean it and make it suitable for analysis. To accurately compare the performance of one model against the others, we have divided the data into training, validation, and testing sets. The performance of the proposed LSTM model is evaluated with the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2) to measure forecasting accuracy. The findings show improved predictive performance: the best-performing LSTM model has an MSE of 8.74, RMSE of 2.96, MAE of 2.35, and R2 of 0.83. These metrics indicate a relationship between the predicted and actual weather conditions, demonstrating the model’s efficiency. The evaluation also shows how hyperparameter optimization, feature selection, and normalization affect model performance and how precise management of weather parameters can result in better forecasts. This research has practical as well as theoretical value because it can improve the dependability of weather forecasts. More precise weather data can benefit crop growing, road networks, transportation systems, and emergency and rescue operations preparing for adverse conditions and disasters. Consequently, this study contributes to research on weather patterns and unexpected rainfall by demonstrating the potential of LSTM networks to analyze key meteorological characteristics for rainfall prediction. Possible future directions include expanding the feature set, using continuous rather than weekly data, and considering a mixed-input approach to increase prediction accuracy. This strategy seeks to improve meteorological forecasting and timely resource allocation and management under climate volatility.
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Mohamed Abd Elmonem Elsebaey
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Ahmed Al-Sayed Ibrahim
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link
https://doi.org/10.54216/JAIM.080105