Forecasting is a broad research domain for multidisciplinary researchers. Forecasting climatic conditions is a challenging task that is carried out by scientists around the globe. The researchers for Forecasting of the various climatic phenomena have used multiple architectures of Artificial Neural Networks (ANN). Such tasks demand huge analysis on recent and past results to give appropriate with precise results of the forecast. The RBF network has a single hidden layer, simpler structure as well as a much faster training process; therefore, it is a popular alternative to the other ANN architectures. In this paper, we are proposing a Radial Basis Function Network (RBFN) as a machine learning tool for making forecasting. In a variety of real-time packages, that includes the prediction of weather, load forecasting, forecasting approximately a variety of traveller and in various programs RBFN applied.

Graphical Abstract

Study of Applications of Radial Basis Function Network in Forecasting