IoT Based Weather Forecasting for Rainfall Prediction Using Intensified Long Short-Term Memory Artificial Neural Network

The purpose of this study was to evaluate the possible uses of artificial neural networks (ANNs) and intensified long short-term memory (LSTM) for rainfall forecasting. The effectiveness of these weather forecasting techniques was evaluated and assigned a score. Materials and Techniques: For the sample test analysis, intensified LSTM and ANN with pre-test power (G-power) of 80% and alpha error rate (Err) of 0.05 were used. The results of the MATLAB simulations show that the ANN has an accuracy of 95.68% and the Intensified LSTM has an accuracy of 87.99%. We discover that the achieved accuracy ratio is 0.034 (p 0.05) using the statistical application SPSS. According to this study, when it comes to advanced weather forecasting, the ANN algorithm outperforms the Intensified LSTM technique in terms of rainfall prediction.