Rainfall Prediction using Hybridized Genetic Algorithm-Based Artificial Neural Network (GAANN) and Genetic Algorithm-Based Support Vector Machine (GA-SVM) Models.

Rainfall forecasting is essential for a few industries, including agriculture, water resource management, and flood forecasts.Rainfall prediction is most important, but now-a-days rainfall forecasting has grown to be a difficult issue.The ability to take safeguards is made possible by accurate rainfall predictions.To anticipate the dependent variables temperature, humidity, location, wind speed, and direction the rainfall prediction is dependent on a few constantly shifting factors.The weather calculation also varies according on the location's geographic characteristics and atmospheric variables.To predict rainfall, this study suggests two hybrid models ANN and SVM based on genetic algorithms.To optimize the hyperparameters and boost prediction accuracy, the genetic algorithm (GA) was combined with ANN and SVM, respectively, to create the GA-ANN and GA-SVM models.The effectiveness of the proposed models was assessed through the application of several statistical metrics, such as Mean Absolute Error (MAE), Correlation Coefficient (CC), and Root Mean Squared Error (RMSE).The results obtained indicate that both GA-ANN and GA-SVM models outperformed the traditional ANN and SVM models, with GA-ANN exhibiting marginally superior performance.Consequently, the suggested hybrid models can be applied to accurately estimate rainfall.The two hybridized models GA-ANN, GA-SVM, are trained and tested using historical rainfall data from a particular region.The study evaluated the performance of both models and compared them to traditional models.The GA-SVM model is a good approach for rainfall prediction, and its accuracy can be further improved by incorporating additional meteorological and environmental factors.The potential of hybridized models that combine genetic algorithms with machine learning techniques to increase the precision of models used to forecast rainfall.In summary, the suggested models help us to improve the prediction of rainfall with better accuracy and gives us better results in agriculture, water resource management, and flood forecasting.