Set-membership estimation from poor quality data sets: Modelling ammonia volatilisation in flooded rice systems. (February 2017)

• Record Type: Journal Article
• Authors: Nurulhuda, K.; Struik, P.C.; Keesman, K.J.
• Is Part Of: Environmental modelling & software. Volume 88(2017)
• Page Start: 138
• Page End: 150
• Publication Date: 2017-02
• Subjects: Set-membership approach -- Bounded-error -- Parameter estimation -- Uncertainty analysis -- Model calibration -- Ammonia volatilisation -- Flooded rice; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.11.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A set-membership (bounded-error) estimation approach can handle small and poor quality data sets as it does not require testing of statistical assumptions which is possible only with large informative data sets. Thus, set-membership estimation can be a good tool in the modelling of agri-environmental systems, which typically suffers from limited and poor quality observational data sets. The objectives of the paper are (i) to demonstrate how six parameters in an agri-environmental model, developed to estimate NH3 volatilisation in flooded rice systems, were estimated based on two data sets using a set-membership approach, and (ii) to compare the set-membership approach with conventional non-linear least-squares methods. Results showed that the set-membership approach is efficient in retrieving feasible parameter-vectors compared with non-linear least-squares methods. The set of feasible parameter-vectors allows the formation of a dispersion matrix of which the eigenvalue decomposition reflects the parameter sensitivity in a region. Highlights: Parameters in a model were estimated from poor quality data sets using sets. The set-membership approach resulted in many parameter-vectors that fit the data. The feasible parameter-vectors encompass estimates from conventional methods. Parameter-vectors eigenvalue decomposition shows regional parameter sensitivity.