Estimation of net surface radiation using eddy flux tower data over a tropical mangrove forest of Sundarban, West Bengal

Abstract

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. 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.