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Comparison of Artificial Intelligence and Physical Models for Forecasting Photosynthetically-Active Radiation

1,2, 1,†, 1,*,†, 2 and 1
1
Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
2
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2018, 10(11), 1855; https://doi.org/10.3390/rs10111855
Received: 22 September 2018 / Revised: 5 November 2018 / Accepted: 16 November 2018 / Published: 21 November 2018
(This article belongs to the Section Atmosphere Remote Sensing)
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Abstract

Different kinds of radiative transfer models, including a relative sunshine-based model (BBM), a physical-based model for tropical environment (PBM), an efficient physical-based model (EPP), a look-up-table-based model (LUT), and six artificial intelligence models (AI) were introduced for modeling the daily photosynthetically-active radiation (PAR, solar radiation at 400–700 nm), using ground observations at twenty-nine stations, in different climatic zones and terrain features, over mainland China. The climate and terrain effects on the PAR estimates from the different PAR models have been quantitatively analyzed. The results showed that the Genetic model had overwhelmingly higher accuracy than the other models, with the lowest root mean square error (RMSE = 0.5 MJ m−2day−1), lowest mean absolute bias error (MAE = 0.326 MJ m−2day−1), and highest correlation coefficient (R = 0.972), respectively. The spatial–temporal variations of the annual mean PAR (APAR), in the different climate zones and terrains over mainland China, were further investigated, using the Genetic model; the PAR values in China were generally higher in summer than those in the other seasons. The Qinghai Tibetan Plateau had always been the area with the highest APAR (8.668 MJ m−2day−1), and the Sichuan Basin had always been the area with lowest APAR (4.733 MJ m−2day−1). The PAR datasets generated by the Genetic model, in this study, could be used in numerous PAR applications, with high accuracy. View Full-Text
Keywords: photosynthetically-active radiation; physical models; artificial neural network; climate zones; terrain features photosynthetically-active radiation; physical models; artificial neural network; climate zones; terrain features
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Feng, L.; Qin, W.; Wang, L.; Lin, A.; Zhang, M. Comparison of Artificial Intelligence and Physical Models for Forecasting Photosynthetically-Active Radiation. Remote Sens. 2018, 10, 1855.

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