Enhancing Rainfall Prediction Using LSTM Algorithm

Abstract

Rainfall is an important factor that influences various aspects of human life, including agriculture, transportation, and urban planning. With climate change, the need for accurate rainfall prediction systems is becoming increasingly urgent. Traditional methods, such as statistical or physical models, often struggle to deal with the complex and nonlinear nature of weather data. This research proposes the use of Long Short-Term Memory (LSTM), a deep learning model capable of processing sequential data, to predict rainfall based on historical data. The model can capture long-term dependencies, making it suitable for analyzing meteorological data such as temperature, humidity, wind speed and rainfall intensity. This paper investigates the performance of an LSTM-based rainfall prediction system, and compares it with traditional forecasting methods. Evaluation metrics such as Root Mean Square Error (RMSE) are used to assess the accuracy of predictions. These findings indicate that LSTM-based models provide a more reliable solution for rainfall prediction, especially in detecting extreme weather events early.