Artificial Neural Network Estimation of Daily Net Radiation Using Meteorological Data in Thailand

Net radiation is the difference between downward and upward radiation, considering both shortwave and longwave radiation. The net radiation controls the water cycle, plant photosynthesis, the earth’s climate changes, and the energy balance. In this paper, the Artificial Neural Network (ANN) model is developed for estimating daily net radiation from meteorological data that are based on maximum air temperature, minimum air temperature, daily relative humidity, and daily solar radiation. Net radiation and meteorological data collected for 7 years (2017-2023) from Chiang Mai meteorological station (CM: 18.77°N, 98.96°E), Ubon Ratchathani meteorological station (UB: 15.24°N, 105.02°E), Nakhon Pathom meteorological station (NP: 14.01°N, 99.96°E), and Songkhla meteorological station (SK: 7.41°N, 100.62°E) were used to train and test the model. The discrepancy between the net radiation estimated by the ANN and the measured net radiation was presented in terms of determination coefficient (R2), relative root mean square error (RMSE), and relative mean bias error (MBE). The model showed 0.98, 14.48%, and -2.17%, respectively. The result shows that the artificial neural network model is an accurate and easy option for estimating surface net radiation.