Set-membership estimation from poor quality data sets: Modelling ammonia volatilisation in flooded rice systems

• Published in: Environmental modelling & software : with environment data news Vol. 88; pp. 138 - 150
• Main Authors: Nurulhuda, K., Struik, P.C., Keesman, K.J.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.02.2017; Elsevier Science Ltd
• Subjects: Ammonia; Ammonia volatilisation; Bounded-error; Datasets; Decomposition; Eigenvalues; Environment models; Environmental modeling; Flooded rice; Hydrologic data; Least squares; Mathematical models; Matrix methods; Model calibration; Parameter estimation; Parameter sensitivity; Set-membership approach; Studies; Uncertainty analysis; Parameter estimation; Model calibration; Ammonia volatilisation; Flooded rice; Bounded-error; Set-membership approach; Uncertainty analysis
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2016.11.002

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. •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.