Accurate Weather Forecasting for Rainfall Prediction Using Artificial Neural Network Compared with Deep Learning Neural Network
D. Vasudeva Rayudu, J. Femila Roseline
Abstract
Aim: This study set out to determine how well AI approaches like Artificial Neural Networks (ANNs) and Deep Learning Neural Networks (DLNNs) might be used to forecast rainfall (DNN). These methods of weather prediction were tested and ranked in terms of their efficiency. Substances and Techniques: Group 1 uses a Deep Learning Neural Network (DNN) for analysis, whereas Group 2 uses an Artificial Neural Network (ANN) with a pre-test power (G-power) of 80% and an alpha error rate (Err) of 0.05 for sample test analysis. In all, 20 samples were identified; 10 from each category. According to the outcomes of the MATLAB simulations, the accuracy of the Deep Learning Neural Network (DNN) is 92.59%, while that of the Artificial Neural Network (ANN) is 95.68%. By means of SPSS statistical program, we find that the achieved accuracy ratio is 0.034 (p 0.05). This study concludes that the unique Artificial Neural Network (ANN) algorithm outperforms the Deep Learning Neural Network (DNN) method in predicting rainfall in the context of cutting-edge weather forecasting.