AM  Vol.5 No.5 , March 2014
Comparison of Machine Learning Regression Methods to Simulate NO3 Flux in Soil Solution under Potato Crops
ABSTRACT

Nitrate (NO3) leaching is a major issue in sandy soils intensively cropped to potato. Modelling could test the effect of management practices on nitrate leaching, particularly with regard to optimal N application rates. The NO3 concentration in the soil solution is well known for its local heterogeneity and hence represents a major challenge for modeling. The objective of this 2-year-study was to evaluate machine learning regression methods to simulate seasonal NO3 concentration dynamics in suction lysimeters in potato plots receiving different N application rates. Four machine learning function approximation methods were compared: multiple linear regressions, multivariate adaptive regression splines, multiple-layer perceptrons, and least squares support vector machines. Input candidates were chosen for known relationships with NO3 concentration. The best regression model was obtained with a 6-inputs least squares support vector machine combining cumulative rainfall, cumulative temperature, day of the year, N fertilisation rate, soil texture, and depth.


Cite this paper
Fortin, J. , Morais, A. , Anctil, F. and Parent, L. (2014) Comparison of Machine Learning Regression Methods to Simulate NO3 Flux in Soil Solution under Potato Crops. Applied Mathematics, 5, 832-841. doi: 10.4236/am.2014.55079.
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