Aleixo, Manam, AM 69077-()()(). Brazil f Ghent University. Department of Applied Mathematics. Biometrics and [>roces~ ControL Coupure Links 653. B-.
US Geological Survey
USGS Staff – Published Research University of Nebraska - Lincoln
Year
Integrated Modelling Frameworks for Environmental Assessment and Decision Support A. E. Rizzoli, IDSIA G. Leavesley, US Geological Survey J. C. Ascough II, USDA-ARS-NPA R. M. Argent, Bureau of Meteorology I. N. Athanasiadis, IDSIA V. Brilhante, University of Amazonas F. H. A. Claeys, Ghent University O. David, US Geological Survey M. Donatelli, IPSC P. Gijsbers, WL Delft Hydraulics D. Havlik, Austrian Research Centers GmbH A. Kassahun, Information Technology Group P. Krause N. W. T. Quinn, University of California H. Scholten, Wageningen University R. S. Sojda, Northern Rocky Mountain Science Center F. Villa, Gund Institute for Ecological Economics
This paper is posted at DigitalCommons@University of Nebraska - Lincoln. http://digitalcommons.unl.edu/usgsstaffpub/196
Published in ENVIRONMENTAL MODELLING, SOFTWARE AND DECISION SUPPORT: STATE OF THE ART AND NEW PERSPECTIVES, edited by A. J. Jakeman, A. A. Voinov, A. E. Rizzoli, & S. H. Chen (Amsterdam et al.: Elsevier, 2008). This article is a U.S. government work and is not subject to copyright in the United States.
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CHAPTER
SEVEN
INTEGRATED MODELLING FRAMEWORKS FOR ENVIRONMENTAL ASSESSMENT AND DECISION SUPPORT A.E. Rizzoli', G. leaves ley b, J.e. Ascough II', R.M. Argent d, I.N. Athanasiadis', V. Brilhante e, F.H.A. Claeys f, O. David b, M. Donatelli g, P. Gijsbers h, D. Havlik t, A. Kassahun i, P. Krause k, N.w.T. Quinn I, H. Scholten m, R.5. Sojda", and F. Villa 0
Contents 7.1.
Introduction 7.1.1 A first definition 7.1.2 Why do we develop new frameworks? 7.1.3 A more insightFul definition
102 103 103
104
p. A Generic Architecture for EIMFs 7.3.
105
7.2.1 A vision Knowledge Representation and Management
107 107
IUSIA, Galleri.l. 2. CH-6928 ManIlO, S"\'vitzerland h US Geological Survey. PO Box 23046, MS 412, Denver Federal Center, Lake\\ood, CO H(l22S, USA USDA-ARS-NPA, Agricultural Sy~tel1l'> Re,earch Unit, 2150 Centre Avenue, mug. n, Suite 2(10, Fort Collim, CO 80526, USA d Bureau of Meteorology, W;ltt"r Divi,ion. GIPO nox 1289, MelbQurne 3001, Australia Federal University of AmazoIlJs, Computing Science Department, Av. Gen. Rodrigo Octavia J. Ramos, 30(lU. Aleixo, Manam, AM 69077-()()(). Brazil f Ghent University. Department of Applied Mathematics. Biometrics and [>roces~ ControL Coupure Links 653. B'}OO() Gent, Belgiulll IPse. Agri4cast Action, JOlin Research Centre, Via E. Fermi, 2749, I-21U27 Ispra (VA), Italy h WL Delft Hydraulics. Inland Water Systems. PO Box 177, 26()() MH, Delft, The Netherlands Smart Sy'>tem'> nivi~ion, Austrian Rrsearcb Centers GmbH, ARC. 2444 5eibersdort: A\l'itria Department of Social Sciences. Information Technology Group. Holland
7.3.
KNOWLEDGE REPRESENTATION AND MANAGEMENT
Formal knowledge representation through ontologies has been suggested as a viable solution for information and knowledge integration problems (Ludaescher et aI., 2001; Villa, 2007) on the grounds that they elicit the meaning of knowledge in ways understandable by both computer systems and humans. An ontology is a formalism for knowledge representation that con1prises a vocabulary of terms representing concepts, properties and relations, knowledge dol11ain characterisation, and formal specifications of the intended meaning of such terms (Uschold and Gruningcr, 1996). As ontologies are founded on logical languages, automated reasoning can be employed in order to ensure model consistency and ontology-compliance. The integration of models and data is the principal problem faced when building EDSSs and IATs. As we know, models and data are intrinsically related: "Science consists of confronting different descriptions of how the world works with data, using the data to arbitrate between the different descriptions, and using the best description to make additional predictions or decisions. These descriptions of how the world nlight work are hypotheses, and often they can be translated into quantitative predictions via models" (Hilborn and Mangel, 1997). In modelling practice, however, to consistently relate data to lnodels is not an easy task because data, while confonning to the sanle paradign1s and world views that inspire model conceptualisations, luay not directly connect to the higher-level set of concepts necessary to describe a l11odel. This difficulty often leads to bias and mismatches between Inodels and supporting data sets.
A.E. Rizzoli et al.
108
Domain Data Ontology
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Figure 7.2
Modelling Onloiogy
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Semantic Data
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Semantic Model Components
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