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Developing Models with More Detail: Do More Algorithms Give More Truth?

Published online by Cambridge University Press:  12 June 2017

Donn G. Decoursey
Donn G. Decoursey*
Affiliation:
U.S. Dep. Agric.-Agric. Res. Serv., Hydro-Ecosystem Research Unit, Fort Collins, CO 80521
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Abstract

Do quasi-physically based models with more detail perform better than regression or other empirical models? This is a question that was raised many years ago and still remains. In an effort to respond to this question, the author reviews the needs and concerns of users and then divides the large number of models into three classes: (1) screening, (2) research, and (3) planning, monitoring, and assessment. Empirical and causal (physically based) models are contrasted and the advantages and disadvantages of each described. Sources of model uncertainty (properties of data bases, model structure, parameter estimation methods, algorithmic implementation, verification and validation, and future users) that lead to skepticism about models' performance are investigated. Simulation scale and spatial variability are also important considerations. The leading question is then discussed from the perspective of screening, research, and planning, monitoring, and regulatory models.

Keywords

Research modelsscreening modelsmodel validationsimulation scalesassessment modelsspatial variabilityregulatory modelsmodel uncertaintymodel structure

Information

Type
Symposium
Information
Weed Technology , Volume 6 , Issue 3 , September 1992 , pp. 709 - 715
Copyright
Copyright © 1990 by the Weed Science Society of America 

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