Rainfall Prediction Using Long Short-Term Memory Model

In meteorological forecasting, accurate rainfall prediction remains a challenging and vital task with profound implications for disaster preparedness, agriculture, and water resource management. Leveraging the advancements in deep learning, this paper presents a novel approach to rainfall prediction using a Long Short-Term Memory (LSTM) Network. We took into consideration extraneous elements connected to weather patterns that influence precipitation. The monthly data are divided into training and testing sets, with the training sets going through preprocessing and organization to create time series models that are trained in each DL model. The mean square error (MSE) and root mean square error (RMSE) are used to assess the prediction performance of the suggested LSTM model, with which error metrics are compared with those of the MLP-based model. Our LSTM model outperforms the baseline model for forecasting time-series rainfall data.