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Inverse Models: A Necessary Next Step in Ground‐Water Modeling
Author(s) -
Poeter Eileen P.,
Hill Mary C.
Publication year - 1997
Publication title -
groundwater
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.84
H-Index - 94
eISSN - 1745-6584
pISSN - 0017-467X
DOI - 10.1111/j.1745-6584.1997.tb00082.x
Subject(s) - inverse , calibration , computer science , identification (biology) , least squares function approximation , inverse problem , obstacle , field (mathematics) , nonlinear regression , nonlinear system , regression analysis , mathematical optimization , data mining , mathematics , statistics , machine learning , geography , mathematical analysis , botany , geometry , archaeology , physics , quantum mechanics , estimator , pure mathematics , biology
Inverse models using, for example, nonlinear least‐squares regression, provide capabilities that help modelers take full advantage of the insight available from ground‐water models. However, lack of information about the requirements and benefits of inverse models is an obstacle to their widespread use. This paper presents a simple ground‐water flow problem to illustrate the requirements and benefits of the nonlinear least‐squares regression method of inverse modeling and discusses how these attributes apply to field problems. The benefits of inverse modeling include: (1) expedited determination of best fit parameter values; (2) quantification of the (a) quality of calibration, (b) data shortcomings and needs, and (c) confidence limits on parameter estimates and predictions; and (3) identification of issues that are easily overlooked during nonautomated calibration.