Artificial neural network models for prediction of net radiation over a tropical region

Abstract

In this study, net radiation and temperature data were collected from the Nigeria Meteorological Agency, Lagos, covering a period of 1983–2013. The study uses a multiple layer perceptron (MLP) model of artificial neural network (ANN) with three algorithms namely the Gradient Descent, Conjugate Gradient, and Broyden–Fletcher–Goldfarb–Shanno for the prediction of net radiation using the temperature series as input variables in place of commonly used empirical statistical methods. The investigation is conducted in a tropical region where 16 meteorological stations spatially distributed across the four climatic regions of Nigeria viz semi-arid (SAR), sub-humid dry (SHD), sub-humid humid (SHH), and humid (HUM) regions were used as a case study. Analyses showed that minimum temperature has a greater positive impact on net radiation than maximum temperature. This has been attributed among other factors to have contributed to higher magnitudes of net radiation in humid regions than those of arid regions. Meanwhile, the performance assessment of the ANN models using the refined index (dr) metric showed that among the three models proposed, the MLP-BFGS model performed best having maximum values of 0.91 in the SAR, 0.65 in the SHD, 0.80 in the SHH, and 0.88 in the HUM regions. It also has the lowest error analysis with magnitudes of root mean square error values of 7.92 W/m2 in the SAR, 9.95 W/m2 in the SHD, 12.91 W/m2 in the SHH, and 8.73 W/m2 in the HUM regions. Finally, it can be inferred from the results that MLP neural networks with the BFGS algorithm can predict net radiation accurately better than any empirical statistical methods over Nigeria.