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

• Published in: Engineering Reports
• Main Authors: Ogunrinde, Akinwale T., Oguntunde, Phillip G., Fasinmirin, Johnson T., Akinwumiju, Akinola S.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2020
• Subjects: General Engineering; General Computer Science; Nash; Sutcliffe; North Atlantic; North Atlantic oscillation
• Online Access: http://dx.doi.org/10.1002/eng2.12194; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Feng2.12194; https://onlinelibrary.wiley.com/doi/pdf/10.1002/eng2.12194; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/eng2.12194

Abstract The 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.