@article{MAKHILLJMMS20115328172,
    title = {Comparative Analysis of Rainfall Prediction Using Statistical Neural Network and Classical Linear Regression Model},
    journal = {Journal of Modern Mathematics and Statistics},
    volume = {5},
    number = {3},
    pages = {66-70},
    year = {2011},
    issn = {1994-5388},
    doi = {jmmstat.2011.66.70},
    url = {https://makhillpublications.co/view-article.php?issn=1994-5388&doi=jmmstat.2011.66.70},
    author = {G.N. and},
    keywords = {Nigeria,NIMET,Rainfall,OLS,ordinary least squares,Statistical Neural Network (SNN),model selection criteria},
    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 <img src="http://docsdrive.com/images/medwelljournals/jmmstat/2011/img8-2k11-66-70.gif" width="34" height="15" align="absmiddle"> 
  , Akaike Information Criterion (AIC) and Schwarz Information Criterion (SIC). 
  Though, the MSE and R<SUP>2</SUP> 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).}
    }