Modeling and prediction of time-varying environmental data using advanced bayesian methods

Majdi Mansouri, Benjamin Dumont, Marie France Destain

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

The problem of state/parameter estimation represents a key issue in crop models, which are nonlinear, non-Gaussian, and include a large number of parameters. The prediction errors are often important due to uncertainties in the equations, the input variables, and the parameters. The measurements needed to run the model and to perform calibration and validation are sometimes not numerous or known with some uncertainty. In these cases, estimating the state variables and/or parameters from easily obtained measurements can be extremely useful. In this chapter, the authors address the problem of modeling and prediction of time-varying Leaf area index and Soil Moisture (LSM) to better handle nonlinear and non-Gaussian processes without a priori state information. The performances of various conventional and state-of-the-art estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and the more recently developed technique Variational Bayesian Filter (VF). The original data was issued from experiments carried out on silty soil in Belgium with a wheat crop during two consecutive years, the seasons 2008-09 and 2009-10.

Original languageEnglish
Title of host publicationExploring Innovative and Successful Applications of Soft Computing
PublisherIGI Global
Pages112-137
Number of pages26
ISBN (Electronic)9781466647862
ISBN (Print)146664785X, 9781466647855
DOIs
Publication statusPublished - 30 Nov 2013
Externally publishedYes

Fingerprint

Crops
Variational techniques
Soil moisture
Extended Kalman filters
Kalman filters
Parameter estimation
Calibration
Soils
Experiments
Uncertainty

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Cite this

Mansouri, M., Dumont, B., & Destain, M. F. (2013). Modeling and prediction of time-varying environmental data using advanced bayesian methods. In Exploring Innovative and Successful Applications of Soft Computing (pp. 112-137). IGI Global. https://doi.org/10.4018/978-1-4666-4785-5.ch007

Modeling and prediction of time-varying environmental data using advanced bayesian methods. / Mansouri, Majdi; Dumont, Benjamin; Destain, Marie France.

Exploring Innovative and Successful Applications of Soft Computing. IGI Global, 2013. p. 112-137.

Research output: Chapter in Book/Report/Conference proceedingChapter

Mansouri, M, Dumont, B & Destain, MF 2013, Modeling and prediction of time-varying environmental data using advanced bayesian methods. in Exploring Innovative and Successful Applications of Soft Computing. IGI Global, pp. 112-137. https://doi.org/10.4018/978-1-4666-4785-5.ch007
Mansouri M, Dumont B, Destain MF. Modeling and prediction of time-varying environmental data using advanced bayesian methods. In Exploring Innovative and Successful Applications of Soft Computing. IGI Global. 2013. p. 112-137 https://doi.org/10.4018/978-1-4666-4785-5.ch007
Mansouri, Majdi ; Dumont, Benjamin ; Destain, Marie France. / Modeling and prediction of time-varying environmental data using advanced bayesian methods. Exploring Innovative and Successful Applications of Soft Computing. IGI Global, 2013. pp. 112-137
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