A comparison of eight metamodeling techniques for the simulation of N2O fluxes and N leaching from corn crops

The environmental costs of intensive farming activities are often under-estimated or not traded by the market, even though they play an important role in addressing future society’s needs. The estimation of nitrogen (N) dynamics is thus an important issue which demands detailed simulation based meth...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2012-06, Vol.34, p.51-66
Main Authors: Villa-Vialaneix, Nathalie, Follador, Marco, Ratto, Marco, Leip, Adrian
Format: Article
Language:English
Subjects:
Agriculture
Environmental Engineering
Environmental Sciences
Mathematics
Metamodeling
N leaching
N2O flux
Neural network
Random forest
Splines
Statistics
Statistics Theory
SVM
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Summary:The environmental costs of intensive farming activities are often under-estimated or not traded by the market, even though they play an important role in addressing future society’s needs. The estimation of nitrogen (N) dynamics is thus an important issue which demands detailed simulation based methods and their integrated use to correctly represent complex and non-linear interactions into cropping systems. To calculate the N2O flux and N leaching from European arable lands, a modeling framework has been developed by linking the CAPRI agro-economic dataset with the DNDC-EUROPE bio-geo-chemical model. But, despite the great power of modern calculators, their use at continental scale is often too computationally costly. By comparing several statistical methods this paper aims to design a metamodel able to approximate the expensive code of the detailed modeling approach, devising the best compromise between estimation performance and simulation speed. We describe the use of two parametric (linear) models and six non-parametric approaches: two methods based on splines (ACOSSO and SDR), one method based on kriging (DACE), a neural networks method (multilayer perceptron, MLP), SVM and a bagging method (random forest, RF). This analysis shows that, as long as few data are available to train the model, splines approaches lead to best results, while when the size of training dataset increases, SVM and RF provide faster and more accurate solutions.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2011.05.003