首页|Effects of Environmental Factors on Ozone Flux over a Wheat Field Modeled with an Artificial Neural Network
Effects of Environmental Factors on Ozone Flux over a Wheat Field Modeled with an Artificial Neural Network
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NSTL
Hindawi Publishing Group
Ozone (O_3) flux-based indices are considered better than O_3 concentration-based indices in assessing the effects of ground O_3 on ecosystem and crop yields. However, O_3 flux (F_o) measurements are often lacking due to technical reasons and environmental conditions. This hampers the calculation of flux-based indices. In this paper, an artificial neural network (ANN) method was attempted to simulate the relationships between F_o and environmental factors measured over a wheat field in Yucheng, China. The results show that the ANN-modeled F_o values were in good agreement with the measured F_o values. The R~2 of an ANN model with 6 routine independent environmental variables exceeded 0.8 for training datasets, and the RMSE and MAE were 3.074 nmol·m~(-2)·s and 2.276 nmol·m~(-2).s for test dataset, respectively. CO_2 flux and water vapor flux have strong correlations with F_o and could improve the fitness of ANN models. Besides the combinations of included variables and selection of training data, the number of neurons is also a source of uncertainties in an ANN model. The fitness of the modeled F_o was sensitive to the neuron number when it ranged from 1 to 10. The ANN model consists of complex arithmetic expressions between F_o and independent variables, and the response analysis shows that the model can reflect their basic physical relationships and importance. O_3 concentration, global radiation, and wind speed are the important factors affecting O_3 deposition. ANN methods exhibit significant value for filling the gaps of F_o measured with micrometeorological methods.
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
NSTL
Hindawi Publishing Group
