Radial basis function neural networks for reliably forecasting rainfall

Abstract

Rainfall forecasting is an interesting task especially in a modern city facing the problem of global warming; in addition rainfall is a necessary input for the analysis and design of hydrologic systems. Most rainfall real-time forecasting models are based on conceptual models simulating the complex hydrological process under climate variability. As there are a lot of variables and parameters with uncertainties and non-linear relationships, the calibration of conceptual or physically based models is often a difficult and time-consuming procedure. Simpler artificial neural network (ANN) forecasts may therefore seem attractive as an alternative model. The present research demonstrates the application of the radial basis function neural network (RBFNN) to rainfall forecasting for Alexandria City, Egypt. A significant feature of the input construction of the RBF network is based on the use of the average 10 year rainfall in each decade to forecast the next year. The results show the capability of the RBF network in forecasting the yearly rainfall and two highest rainfall monsoon months, January and December, compared with other statistical models. Based on these results, the use of the RBF model can be recommended as a viable alternative for forecasting the rainfall based on historical rainfall recorded data.