Process-based simulation library SALMO-OO for lake ecosystems. Part 2.pdf

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Process-based simulation library SALMO-OO for lake ecosystems. Part 2.pdf

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Process-based simulation library SALMO-OO for lake ecosystems. Part 2

E C O L O G I C A L I N F O R M A T I C S 3 ( 2 0 0 8 ) 1 8 1 – 1 9 0 ava i l ab l e a t www.sc i enced i r ec t . com www.e l sev i e r. com/ loca te / eco l i n fProcess-based simulation library SALMO-OO for lake ecosystems. Part 2: Multi-objective parameter optimization by evolutionary algorithmsHongqing Cao?, Friedrich Recknagel, Lydia Cetin, Byron Zhang School of Earth and Environmental Sciences, University of Adelaide, 5005, AustraliaA R T I C L E I N F O? Corresponding author. E-mail address: hxc@cs.nott.ac.uk (H. Cao). 1574-9541/$ – see front matter ? 2008 Elsevi doi:10.1016/j.ecoinf.2008.02.001A B S T R A C TArticle history: Received 29 November 2006 Received in revised form 15 January 2008 Accepted 8 February 2008SALMO-OO represents an object-oriented simulation library for lake ecosystems that allows to determine generic model structures for certain lake categories. It is based on complex ordinary differential equations that can be assembled by alternative process equations for algal growth and grazing as well as zooplankton growth and mortality. It requires 128 constant parameters that are causally related to the metabolic, chemical and transport processes in lakes either estimated from laboratory and field experiments or adopted from the literature. An evolutionary algorithm (EA) was integrated into SALMO-OO in order to facilitate multi- objective optimization for selected parameters and to substitute them by optimum temperature and phosphate functions. The parameters were related to photosynthesis, respiration and grazing of the three algal groups diatoms, green algae and blue-green algae. The EA determined specific temperature and phosphate functions for same parameters for 3 lake categories that were validated by ecological data of six lakes from Germany and South Africa. The results of this study have demonstrated that: (1) the hybridization of ordinary differential equations by EA provide a sophisticated approach to fine-tune crucial parameters of