A toolbox to find the best mechanistic model to predict the behavior of environmental systems. (September 2016)

• Record Type: Journal Article
• Authors: van Turnhout, André G.; Kleerebezem, Robbert; Heimovaara, Timo J.
• Is Part Of: Environmental modelling & software. Volume 83(2016:Sep.)
• Page Start: 344
• Page End: 355
• Publication Date: 2016-09
• Subjects: Bayesian inference -- Mechanistic biogeochemical modeling -- Integrative assessment of environmental systems -- Qualitative and quantitative model selection -- Optimal data description; 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.2016.05.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Reliable prediction of the long-term behavior of environmental systems such as Municipal Solid Waste (MSW) landfills is challenging. While many driving forces influence this behavior, characterization of them is limited by measurement techniques. Therefore, a model structure for reliable prediction needs to optimally combine all measured information with suitable mechanistic information from literature. How to get such an optimal model structure? This study presents a toolbox to find and build the model structure that describes an environmental system as close as possible. The toolbox combines environmental frameworks to include all suitable mechanistic information; it fully couples kinetic and equilibrium reactions and contains multiple resources to obtain biogeochemical parameters. Several possible optimal model structures are quickly built and evaluated with objective statistical performance criteria obtained via Bayesian inference. By applying the novel methodology, we select the best model structure for anaerobic digestion of MSW in full scale landfills. Graphical abstract: Highlights: Finding the best mechanistic model to predict the behavior of environmental systems. The toolbox allows to quickly assess multiple biogeochemical reaction networks. Coupled kinetic & equilibrium processes and mechanistic parameter resources. Networks are evaluated with objective statistical criteria given measured data. The toolbox enabled to find the optimal network for anaerobic digestion of MSW.