Combining the Group Method of Data Handling with Firefly Algorithm for Rainfall Forecasting (International Journal of Innovative Computing, 2026)
Rainfall forecasting continues with the evolution of mixed and new research, where a variety of the same models have been suggested to make some improvements in prediction validity. However, at the same time there is a lack of models that seems to be single capable of handling all features of the data to the best of its ability, with multi-method selection difficult. One way to overcome this constraint is that model combination methods have rapidly emerged in time series forecasting, especially precipitation forecasting. These can be an array of simple but complex model pairs. This study proposes a rainfall forecasting framework for the work based on Group Method of Data Handling (GMDH) and Firefly Algorithm (FA). Forecasting is first of all done based on the GMDH (with polynomial transfer function, GMDH-Poly). Then the model is further enlarged with transfer functions (Sigmoid function, Tangent function, and Radial Basis Function). After that, FA is used to heuristically update the weights of each GMDH variant, taking into account the output from this optimisation for a better prediction. This proposed approach is applied in the case of rainfall data in Kuching, Sarawak for the years 2010–2019. The experimental results clearly show the hybrid GMDH-FA model significantly outperforms the standard GMDH model. In particular, GMDH-FA scoring RMSE, MAE and MAPE is 0.0550, 0.0455 and 24.0733 respectively, which is significantly lower than that of, GMDH-Poly (0.0981, 0.0761 and 47.3864) in testing data set.
Keywords: Group method of data handling, GMDH-FA, forecasting