A Long Short-Term Memory Approach for Weather Forecasting

Weather forecasting involves using weather data to predict the future weather conditions in a specific location. Understanding the weather is important as it affects various things such as planting crops, running a business, and being prepared for emergencies. Farmers rely on precise weather forecasts to determine the best time for planting, while businesses use them to organize their operations, and communities depend on them to stay secure. This study examines the application of Long Short-Term Memory (LSTM) in forecasting weather. LSTM is a neural network known for effectively interpreting and processing sequential data, like a sequence of climate observations. By adjusting parameters such as batch size, number of epochs, and optimizer algorithm, the accuracy of the predictions changes in the updated results.