Evapotranspiration Estimation Using Artificial Neural Network over South-Western Nigeria

Abstract

This study was carried out to estimate evapotranspiration over the South-Western region of Nigeria, Artificial Neural Network was used for the estimation of Evapotranspiration over South-Western Nigeria. Using a 36 years meteorological data of South-Western Nigeria obtained from NASA (National Aeronautics and Space Administration) Power data, the Multilayer Perceptron Neural Network and Radial Basis Function Neural Network under several Neural Network Architecture was used, training, testing and validation operations also were performed for estimating evapotranspiration closely to the target calculated value. The performance of each neural network under several NN Architecture was evaluated using statistical indicator such as R (Correlation of Coefficient), R2 (Coefficient of Determination), MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). Results present Multilayer Perceptron Neural Network the best neural network with about 70% of its R-values (0.70) because ETo varies in the same pattern as the four of the input parameters used (minimum and maximum air temperature, solar radiation, and wind speed) compare to Radial Basis Network that has 50% of its R-values below (0.70) under several NN Architecture because of the inverse relationship and poor correlation of the ETo and relative humidity. Also, LAGOS and OYO dataset produced the highest performance with an R-value of (0.999998) as a result of uniformity in the climatic trend over 36 years while OGUN dataset produced the lowest performance of (0.467169) as a result of significant variation in the climatic trend over the past 36 years. The study presented here has profound implications for future studies of estimating evapotranspiration and one day help solve the problem of water scarcity and food insecurity.