Assessment of Neural Networks Performance in Modeling Rainfall Amounts

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dc.contributor.authorAbdulkadir, T.S.
dc.contributor.authorSalami, A.W
dc.contributor.authorAremu, A.S.
dc.contributor.authorAyanshola, A.M.
dc.contributor.authorOyejobi, D.O.
dc.date.accessioned2020-05-29T12:35:10Z
dc.date.available2020-05-29T12:35:10Z
dc.date.issued2017-03
dc.description.abstractThis paper presents the evaluation of performance of Neural Network (NN) model in predicting the behavioral pattern of rainfall depths of some locations in the North Central zones of Nigeria. The input to the model is the consecutive rainfall depths data obtained from the Nigerian Meteorological (NiMET) Agency. The neural networks were trained using neural network toolbox in MATLAB with fifty years (1964–2014) total monthly historical data of five locations while two other locations, Abuja and Lafia with twenty-nine years (1986-2014) and eleven years (2004-2014) total monthly data respectively. Analysis showed the variation in the values of correlation coefficients (R) for each location of the study area in response to change in number of hidden neurons. The average R values of 0.80, 0.62, 0.65, 0.67, 0.79, 0.76 and 0.81 with corresponding mean square errors of 2.12, 0.23, 0.26, 0.36, 2.61, 1.18 and 1.03 were obtained for Abuja, Makurdi, Ilorin, Lokoja, Lafia, Minna and Jos respectively. The results showed some slight variability in the performances of NN due to changes in the number of hidden neurons during the network training. These values of R indicated that the networks are fit to be used for the subsequent quantitative prediction of rainfall depths in each location which is useful for safeguarding against future flood and drought occurrence in the North Central zone, Nigeria.en_US
dc.identifier.citationAbdulkadir T.S., Salami A.W., Aremu A.S., Ayanshola A.M., & Oyejobi D.O. (2017): Assessment of Neural Networks Performance in Modeling Rainfall Amounts, Journal of Research in Forestry, Wildlife & Environment. 9(1); 12-22, Published by Department of Forestry, Wildlife and Range Management, University of Agriculture, Makurdi, Nigeria, Available online at: https://www.ajol.info/index.php/jrfwe/issue/view/15726en_US
dc.identifier.urihttp://hdl.handle.net/123456789/4076
dc.language.isoenen_US
dc.publisherDepartment of Forestry, Wildlife and Range Management, University of Agriculture, Makurdi, Nigeriaen_US
dc.subjectRainfall depthsen_US
dc.subjectNeural Networksen_US
dc.subjectcoefficient of correlationen_US
dc.subjectmean square errorsen_US
dc.titleAssessment of Neural Networks Performance in Modeling Rainfall Amountsen_US
dc.typeArticleen_US

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