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Automated Parameterization of Land Surface Process Models Using Fuzzy Logic
Author(s) -
Mackay D S,
Samanta S,
Ahl D E,
Ewers B E,
Gower S T,
Burrows S N
Publication year - 2003
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/1467-9671.00134
Subject(s) - fuzzy logic , estimation theory , measure (data warehouse) , process (computing) , identification (biology) , surface (topology) , calibration , computer science , data mining , mathematics , algorithm , mathematical optimization , statistics , artificial intelligence , biology , operating system , botany , geometry
All land surface process models require parameters that are proxies for spatial processes that are impractical or impossible to measure. Recent developments in model parameter estimation theory suggest that information obtained from calibrating such models is inherently uncertain in nature. As a consequence, identification of optimum parameter values is often highly non–specific. A calibration framework using fuzzy logic is presented to deal with such uncertain information. An application of this technique to calibrate the sub–canopy controls on transpiration in a land surface process model demonstrates that objective estimates of parameter values and expected ranges of predictions can be obtained with suitable choices for objective functions. An iterative refinement in parameter estimates was possible with conditional sampling techniques. The automated approach was able to correctly identify parameter tradeoffs such that two strongly different sets of parameters could