Application of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria

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dc.contributor.authorOgunrinde, Akinwale T.
dc.contributor.authorOguntunde, Phillip G.
dc.contributor.authorFasinmirin, Johnson T.
dc.contributor.authorAkinwumiju, Akinola S.
dc.date.accessioned2026-04-14T08:39:08Z
dc.date.available2026-04-14T08:39:08Z
dc.date.issued2020-07
dc.descriptionFull Text Article
dc.description.abstractThe necessity to perform an accurate prediction of future characteristics of drought requires a robust and efficient technique that can deduce from historical data the stochastic relationship or dependency between history and future. In this study, the applicability of the artificial neural network (ANN) is used for forecasting the standardized precipitation and evapotranspiration index (SPEI) at 12-month timescale for five candidate stations in Nigeria using predictive variable data from 1985 to 2008 (training) and tested data between 2009 and 2015. The predictive variables are monthly average precipitation, average air temperature, maximum temperature, minimum temperature, mean speed, mean solar radiation, sunshine hours, and two large-scale climate indices (Southern Oscillation Index and North Atlantic Oscillation). From the several combinations of the input variables, training algorithms, hidden, and output transfer functions, a total of eight model runs stood out using a three-layer ANN network. The most efficient ANN model architecture had 9,8,1 as the input, hidden, and output neurons, respectively, trained using the Levenberg-Marquardt training algorithm and tansig as the activation and hidden transfer functions. Assessment on the efficiency of the model based on statistics indicate that the coefficient of determination, root mean square error, Nash-Sutcliffe coefficient of efficiency and the mean absolute error ranges between 0.51 and 0.82; 0.57 and 0.75; 0.28 and 0.79; 0.44 and 0.56, respectively, during the testing period. The output of these findings shows that ANN modeling technique can play a significant role as a data-driven model in forecasting monthly SPEI time series and drought characteristics in the study area, thereby leading to the development of an early warning system for the country
dc.identifier.citationOgunrinde, A. T., Oguntunde, P. G., Fasinmirin, J. T., & Akinwumiju, A. S. (2020). Application of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria. Engineering Reports, 2(7), e12194. https://doi.org/10.1002/eng2.12194
dc.identifier.uri. https://doi.org/10.1002/eng2.12194
dc.identifier.urihttps://repository.elizadeuniversity.edu.ng/handle/123456789/1573
dc.language.isoen
dc.publisherJohn Wiley & Sons, Inc.
dc.relation.ispartofseriesVol.2, No.7
dc.titleApplication of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria
dc.typeArticle
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