Hybrid artificial intelligence models based on adaptive neuro fuzzy inference system and metaheuristic optimization algorithms for prediction of daily rainfall

In this study, our primary objective is the accurate prediction of daily rainfall through the application of various hybrid Artificial Intelligence (AI) methods. These methods are grounded in the Adaptive Neuro Fuzzy Inference System (ANFIS) and employ metaheuristic optimization algorithms, specifically the Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Simulated Annealing (SA). The meteorological data utilized for modeling rainfall prediction is sourced from Hoa Binh province in Vietnam. To enhance the datasets, we employed the SavitzkyGolay filtering as a preprocessing noise reduction technique. The performance of the models was assessed using well-established statistical error criteria, including the Correlation Coefficient (R), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other contingency scores. Our findings indicate that the ANFIS-ABC hybrid model outperforms other models, such as ANFIS-SA and ANFIS-GA, in predicting daily rainfall, achieving an R value of 0.82 with raw data and 0.9 with preprocessed data. Notably, the results underscore the positive impact of employing noise reduction through data preprocessing, enhancing the predictive quality of the hybrid models. This study holds significance in the realm of water resource and land management, offering valuable insights for the accurate prediction of daily rainfall.