Weather Prediction using Long Short Term Memory (LSTM) model

This study has designed an AI model to focus on the element design rather than information pre-processing in the weather prediction models. Various climate boundaries were gathered from the public environment server via accessible programming applications by using Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) in various applications executed on the AWS AI stage. In addition to the existing weather forecasting techniques, neural networks are developed by using various combinations of climate boundaries, for example, temperature, precipitation, dampness, wind speed, and strain. Following the preparation of the LSTM model with these boundaries, regardless of the outcomes and performance measurements to train the model, the aftereffects of the climate pattern has been monitored based on two qualities (positive or negative). Here, the climate data is captured via time series data, and further the most recent Long ShortTerm Memory (LSTM) layered model has also been investigated.