Evaluating the effectiveness of advanced LSTM variants in weather forecasting prediction models

Predicting weather is a challenging task due to variability in atmospheric events, although prediction is important in day-to-day life as it ensures protection of individuals and belongings by providing timely alerts. ML and DL are the topmost techniques to identify the complex patterns in large datasets, like satellite data and historical weather records. In time-series forecasting of weather, Deep learning models, basically Long Short-Term Memory (LSTM), have achieved robust performance. This paper covers the different types of LSTM models of weather prediction and also explores LSTM variants particularly Vanilla LSTM, Convolutional LSTM (ConvLSTM), Bidirectional LSTM (BiLSTM) Attention-based LSTM and hybrid ConvLSTM with Attention extending multiple temporal scales to assess their suitability for weather prediction from nowcasting, short, medium, and long-term meteorological predictions. This study aims to provide direction for choosing LSTM variants depending on diverse time scales by summarizing it. Bidirectional LSTM offered a strong balance between computational accuracy and efficiency where faster training and inference are required. It helps to capture both past and future contextual dependencies that provides an advantage in short- to medium-term forecasting.