Comparison of modelling ANN and ELM to estimate solar radiation over Turkey using NOAA satellite data

In this study, solar radiation SR is estimated at 61 locations with varying climatic conditions using the artificial neural network ANN and extreme learning machine ELM. While the ANN and ELM methods are trained with data for the years 2002 and 2003, the accuracy of these methods was tested with data for 2004. The values for month, altitude, latitude, longitude, and land-surface temperature LST obtained from the data of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer NOAA-AVHRR satellite are chosen as input in developing the ANN and ELM models. SR is found to be the output in modelling of the methods. Results are then compared with meteorological values by statistical methods. Using ANN, the determination coefficient R2, mean bias error MBE, root mean square error RMSE, and Willmott’s index WI values were calculated as 0.943, −0.148 MJ m−2, 1.604 MJ m−2, and 0.996, respectively. While R2 was 0.961, MBE, RMSE, and WI were found to be in the order 0.045 MJ m−2, 0.672 MJ m−2, and 0.997 by ELM. As can be understood from the statistics, ELM is clearly more successful than ANN in SR estimation.