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Estimation Of Net Surface Radiation Using Eddy Flux Tower Data Over A Tropical Mangrove Forest Of Sundarban, West Bengal

Title: Estimation Of Net Surface Radiation Using Eddy Flux Tower Data Over A Tropical Mangrove Forest Of Sundarban, West Bengal.
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Name(s): Mahalakshmi, D. V., author
Paul, Arati, author
Dutta, D., author
Ali, M. M., author
Dadhwal, V. K., author
Reddy, R. Suraj, author
Jha, C. S., author
Sharma, J. R., author
Type of Resource: text
Genre: Text
Date Issued: 2016
Physical Form: computer
online resource
Extent: 1 online resource
Language(s): English
Abstract/Description: In this study, net surface radiation (R-n) was estimated using artificial neural network (ANN) and Linear Model (LM). Then, estimated R-n with both the models (ANN and LM) were compared with measured R-n from eddy covariance (EC) flux tower. The routinely measured meteorological variables namely air temperature, relative humidity and wind velocity were used as input to the ANN and global solar radiation as input to the LM. All the input data are from the EC flux tower. Sensitivity analysis of ANN with all the meteorological variables is carried out by excluding one by one meteorological variable. The validation results demonstrated that, ANN and LM estimated R-n values were in good agreement with the measured values, with root mean square error (RMSE) varying between 21.63 W/m(2) and 34.94 W/m(2), mean absolute error (MAE) between 17.93 W/m2 and 22.28 W/m(2) and coefficient of residual mass (CRM) between -0.007 and -0.04 respectively. Further we have computed modelling efficiency (0.97 for ANN and 0.99 for LM) and coefficient of determination (R-2 = 0.97 for ANN and 0.99 for LM) for both the models. Even though both the models could predict R-n successfully, ANN was better in terms of minimum number of routinely measured meteorological variables as input. The results of the ANN sensitivity analysis indicated that air temperatuere is the more important parameter followed by relative humidity, wind speed and wind direction.
Identifier: FSU_libsubv1_wos_000381162400001 (IID), 10.15233/gfz.2016.33.5 (DOI)
Keywords: artificial neural network, artificial neural-networks, china, eddy flux tower, energy-balance archive, global solar-radiation, land, linear model, models, net surface radiation, photosynthetically active radiation, Turkey
Publication Note: The publisher's version of record is available at https://doi.org/10.15233/gfz.2016.33.5
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000381162400001
Owner Institution: FSU
Is Part Of: Geofizika.
0352-3659
Issue: iss. 1, vol. 33

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Mahalakshmi, D. V., Paul, A., Dutta, D., Ali, M. M., Dadhwal, V. K., Reddy, R. S., … Sharma, J. R. (2016). Estimation Of Net Surface Radiation Using Eddy Flux Tower Data Over A Tropical Mangrove Forest Of Sundarban, West Bengal. Geofizika. Retrieved from http://purl.flvc.org/fsu/fd/FSU_libsubv1_wos_000381162400001