Soil NO emissions modelling using artificial neural network

Soils are considered as an important source for NO emissions, but the uncertainty in quantifying these emissions worldwide remains large due to the lack of field experiments and high variability in time and space of environmental parameters influencing NO emissions. In this study, the development of a relationship for NO flux emission from soil with pertinent environmental parameters is proposed. An Artificial Neural Network (ANN) is used to find the best non-linear regression between NO fluxes and seven environmental variables, introduced step by step: soil surface temperature, surface water filled pore space, soil temperature at depth (20-30 cm), fertilisation rate, sand percentage in the soil, pH and wind speed. The network performance is evaluated each time a new variable is introduced in the network, i. e. each variable is justified and evaluated in improving the network performance. A resulting equation linking NO flux from soil and the seven variables is proposed, and shows to perform well with measurements (R-2 = 0.71), whereas other regression models give a poor correlation coefficient between calculation and measurements (R-2 <= 0.12 for known algorithms used at regional or global scales). ANN algorithm is shown to be a good alternative between biogeochemical and large-scale models, for future application at regional scale.