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

• Published in: Environmental science and pollution research international Vol. 30; no. 40; pp. 92555 - 92567
• Main Authors: P, Lathika, D, Sheeba Singh
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Aquatic Pollution; Atmospheric Protection/Air Quality Control/Air Pollution; Bayes Theorem; Bayesian analysis; Climatic conditions; Correlation analysis; Correlation coefficient; Correlation coefficients; Earth and Environmental Science; Economic factors; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Forecasting; Humans; Mathematical models; Meteorological data; Meteorology; Optimization; Optimization algorithms; Prediction models; Rain; Rainfall; Research Article; Separation; Stochasticity; Waste Water Technology; Water Management; Water Pollution Control; Weather; Rainfall; Weather dataset; Bayesian optimization algorithm; Hybrid stochastic; Prediction
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-28734-z

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.