Latent heat and sensible heat flux simulation in maize using artificial neural networks

The results showed high correlation between actual and estimated data; the R2 values for LE flux in irrigated and rain-fed sites were 0.9576, and 0.9642; and for H flux 0.8001, and 0.8478, respectively. Furthermore, the RMSE values ranged from 0.0580 to 0.0721 W/m2 for LE flux and from 0.0824 to 0.0863 W/m2 for H flux. In addition, the sensitivity of the fluxes with respect to each input was analyzed over the growth stages so that the most powerful effects among the inputs for LE flux were identified as net radiation, leaf area index, vapor pressure deficit, and wind speed, and for H flux net radiation, wind speed, air temperature, leaf area index and vapor pressure deficit. Furthermore, to achieve the minimal set of input in order to speed up the analysis procedures, the impact intensity of each input on the fluxes was recognized by deactivation of each input vector in network training. This study reveals that artificial neural networks are not only a powerful technique for estimation of LE and H fluxes, but also can identify the effectiveness of each input on the fluxes.