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Comparative analysis of rainfall prediction using statistical neural network and classical linear regression model
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 a...
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2011
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/5329 | ||
| 042 | |a dc | ||
| 720 | |a Udomboso, C. G. |e author | ||
| 720 | |a Amahia., G. N. |e author | ||
| 260 | |c 2011 | ||
| 520 | |a 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 R2 (R-2 ), 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). | ||
| 024 | 8 | |a 1994-5388 | |
| 024 | 8 | |a ui_art_udomboso_comparative_2011 | |
| 024 | 8 | |a Journal of Modern Mathematics and Statistics 5(3), pp. 66-70 | |
| 024 | 8 | |a http://ir.library.ui.edu.ng/handle/123456789/5329 | |
| 653 | |a Rainfall | ||
| 653 | |a Ordinary least squares | ||
| 653 | |a Statistical Neural Network (SNN) | ||
| 653 | |a Model selection criteria | ||
| 653 | |a OLS | ||
| 653 | |a NIMET | ||
| 653 | |a Nigeria | ||
| 245 | 0 | 0 | |a Comparative analysis of rainfall prediction using statistical neural network and classical linear regression model |