Journal of Modern Mathematics and Statistics
Comparative Analysis of Rainfall Prediction Using Statistical Neural Network and Classical Linear Regression Model
Authors : C.G. Udomboso and G.N. Amahia
Abstract: Different types of models have been used in modeling rainfall.
Since 1990s however, interest has shifted from traditional models to ANN in
rainfall modeling. Many researchers found out that the ANN performed better
than such traditional models. In this study, we compared a traditional linear
model and ANN in the modeling of rainfall in Ibadan, Nigeria. Ibadan is a city
in West Africa, located in the tropical rainforest zone, using the data obtained
from the Nigeria Meteorological (NIMET) station. Three variables were considered
in this study rainfall, temperature and humidity. In selecting between the two
models, we concentrated on the choice of adjusted 
, Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC).
Though, the MSE and R2 were also used, it was concluded from results
that MSE is not a good choice for model selection. This is due to the nature
of the rainfall data (which has wide variations). It was found that the Statistical
Neural Network (SNN), generally performed better than the traditional (OLS).
How to cite this article:
C.G. Udomboso and G.N. Amahia, 2011. Comparative Analysis of Rainfall Prediction Using Statistical Neural Network and Classical Linear Regression Model. Journal of Modern Mathematics and Statistics, 5: 66-70.