A novel model for rainfall prediction using hybrid stochastic-based Bayesian optimization algorithm

Rainfall forecasting is considered one of the key concerns in the meteorological department because it is related strongly to social as well as economic factors. But, because of modern context of climatic conditions and the intense activities of humans, the forecasting procedure of rainfall patterns becomes more problematic. Therefore, this paper proposes a novel timely and reliable rainfall prediction model using a hybrid stochastic Bayesian optimization approach (HS-BOA). The weather dataset containing different meteorological geographical features is provided as input to the introduced prediction method. Hybrid stochastic (HS) specifications are tuned by the Bayesian optimization algorithm (BOA) to upgrade the prediction accuracy. The weather data are initially preprocessed through the pipelines, namely, data separation, missing value prediction, weather condition cod separation, and normalization. After preprocessing, the highly correlated features are removed by correlation matrix using the Pearson correlation coefficient. Then, the most significant features which contribute more to predicting rainfall are selected through the feature selection process. At last, the suggested rainfall forecasting model accurately predicts rainfall using optimized parameters. The experimental analysis is performed, and for the proposed HS-BOA, MAE, RMSE, and COD, values attained for rainfall prediction are 0.513 mm, 59.90 mm, and 40.56 mm respectively. As a result, the proposed HS-BOA approach achieves minimum error rates with increased prediction accuracy than other existing approaches.