Artificial Neural Network Estimation of Daily Net Radiation Using Meteorological Data in Thailand
DOI:
https://doi.org/10.69650/rast.2025.259017Keywords:
Net Radiation , Artificial Neural Network , Meteorological DataAbstract
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.
References
Ojo, O. S., Emmanuel, I., Adeyemi, B. and Ogolo, E. O., Effect of the radiation balance on warming occurrence over west Africa. Scientific African. 11 (2021) 1-17, doi: https://doi.org/10.1016/j.sciaf.2021.e00700.
Da Cunha, A. R., Schöffel, E. R. and Volpe, C. A., Estimation of evapotranspiration by various net radiation estimation formular for non-irrigated grass in Brazil. Journal of Water Resource and Protection. 6 (2014) 1-11, doi: https://doi.org/10.4236/jwarp.2014.615131.
Lorenz, D. J., DeWeaver, E. T. and Vimont, D. J., Evaporation change and global warming: The role of net radiation and relative humidity. Journal of Geophysical Research: Atmospheres. 115 (2010) 1-13, doi: https://doi.org/10.1029/2010JD013949.
Wang, K. and Liang, S., Estimation of daytime net radiation from shortwave radiation measurements and meteorological observations. Journal of Applied Meteorology and Climatology. 48 (2009) 634-643, doi: https://doi.org/10.1175/2008JAMC1959.1.
Irmak, S., Irmak, A., Jones, J. W., Howell, T. A., Jacobs, J. M., Allen, R. G. and Hoogenboom, G., Predicting daily net radiation using minimum climatological data. Journal of Irrigation and Drainage Engineering. 129(4) (2003) 256-269, doi: https://doi.org/10.1061/(ASCE).07339437(2003)129:4(256).
Alados, I., Foyo-Moreno, I., Olmo, F. J. and Alados-Arboledas, L., Relationship between net radiation and solar radiation for semi-arid shrub-land. Agricultural and Forest Meteorology. 116(3-4) (2003) 221-227, doi: https://doi.org/10.1016/S0168-1923(03)00038-8.
Carmona, F., Rivas, R. and Caselles, V., Development of a general model to estimate the instantaneous, daily, and daytime net radiation with satellite data on clear-sky days. Remote Sensing of Environment. 171 (2015) 1-13, doi: https://doi.org/10.1016/j.rse.2015.10.003.
Limhoon, P. and Bualert, S., Variation of net radiation and solar spectrum in Thailand. International Journal of Environmental Science and Development. 4(2) (2013), doi: https://doi:10.7763/IJESD.2013.V4.315.
Tohsing, K., Phoemwong, C., Uearsri, C. and Saiplang, P., An estimation of net radiation from global solar radiation in the main regions of Thailand. Journal of Physics: Conference Series. 2431 (2023) 1-4, doi: https:// doi.10.1088/1742-6596/2431/1/012021.
Ferreira, A. G., Soria-Olivas, E., López, A. J. S. and Lopez-Baeza, E., Estimating net radiation at surface using artificial neural networks: a new approach. Theoretical and applied climatology. 106 (2011) 263-279, doi: https:// doi.10.1007/s00704-011-0488.
Jiang, B., Zhang, Y., Liang, S., Zhang, X. and Xiao, Z., Surface daytime net radiation estimation using artificial neural networks. Remote Sensing, 6(11) (2014) 11031-11050, doi: https:// doi:10.3390/rs61111031.
Aweda, F. O. and Adebayo, S., Estimation of net radiative measurement of meteorological parameters at Iwo, Nigeria in 2018. Zimbabwe Journal of Science and Technology, 15 (2020) 23-33.
Ozturk, M., Salman, O. and Koc, M., Artificial neural network model for estimating the soil temperature. Canadian Journal of Soil Science. 91(4) (2011) 551-562, doi: https://doi.org/10.4141/cjss10073.
Patel, D., Patel, S., Patel, P. and Shah, M., Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study. Environmental Science and Pollution Research. 29 (2022) 32428-32442, doi: https://doi:10.1007/s11356-022-19185-z.
Bishop, C. M. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
Khanna, T. Foundations of Neural Networks. Addison-Addison-Wesley, 1990.
Haykin, S. Neural Networks and Learning Machines. 3rd edn., Pearson Education India, 2009.
Goodfellow, I., Bengio, Y., and Courville, A. Deep Learning. MIT Press, 2016.
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