Estimation of net surface radiation from eddy flux tower measurements using artificial neural network for cloudy skies
| Autor: | Dibyendu Dutta, Chandra Shekhar Jha, J. R. Sharma, Vinay Kumar Dadhwal, M. M. Ali, Rodda Suraj Reddy, D. V. Mahalakshmi, Arati Paul |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Artificial neural network
Environmental Engineering Coefficient of determination 010504 meteorology & atmospheric sciences Meteorology Mean squared error 0211 other engineering and technologies Eddy covariance Meteorological parameters 02 engineering and technology Radiation 01 natural sciences lcsh:TD1-1066 Net surface radiation Sensitivity (control systems) lcsh:Environmental technology. Sanitary engineering Waste Management and Disposal 021101 geological & geomatics engineering 0105 earth and related environmental sciences Water Science and Technology Renewable Energy Sustainability and the Environment Levenberg–Marquardt algorithm Pollution Environmental science Tower |
| Zdroj: | Sustainable Environment Research, Vol 26, Iss 1, Pp 44-50 (2016) |
| ISSN: | 2468-2039 |
| Popis: | Accurate knowledge of net surface radiation (NSR) is required to understand the soil-vegetation-atmosphere feedbacks. However, NSR is seldom measured due to the technical and economical limitations associated with direct measurements. An artificial neural network (ANN) technique with Levenberg–Marquardt learning algorithm was used to estimate NSR for a tropical mangrove forest of Indian Sundarban with routinely measured meteorological variables. The root mean square error (RMSE), mean absolute error (MAE), modelling efficiency (ME), coefficient of residual mass (CRM) and coefficient of determination (R 2 ) between ANN estimated and measured NSR were 37 W m −2 , 26 W m −2 , 0.95, 0.017 and 0.97 respectively under all-weather conditions. Thus, the ANN estimated NSR values presented in this study are comparable to those reported in literature. Further, a detailed study on the estimated NSR for cloudy skies was also analysed. ANN estimated NSR values were compared with in situ measurements for cloudy days and non-cloudy days. The RMSE, MAE and CRM of the model decrease to half when considering the non-cloudy days. Thus, the results demonstrate that major source error in estimating NSR comes from the cloudy skies. Sensitivity of input variables to NSR was further analysed. |
| Databáze: | OpenAIRE |
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