zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
Calibration of flow and water quality modeling using genetic algorithm. (English) Zbl 1032.68745
McKay, Bob (ed.) et al., AI 2002: Advances in artificial intelligence. 15th Australian joint conference on artificial intelligence, Canberra, Australia, December 2-6, 2002. Proceedings. Berlin: Springer. Lect. Notes Comput. Sci. 2557, 720 (2002).
Summary: In mathematical simulation for flow prediction and water quality management, the inappropriate use of any model parameters, which cannot be directly acquired from measurements, may introduce large errors or result in numerical instability. In this paper, the use of a genetic algorithm for determining an appropriate combination of parameter values in flow and water quality modeling is presented. The percentage error of peak value, peak time, and total volume of flow and water quality constituents are important performance measures for model prediction. The parameter calibration is based on field data of tidal as well as water quality constituents collected over five year span from 1991 to 1995 in Pearl River. Another two-year records from 1996 to 1997 are utilized to verify these parameters. Sensitivity analysis on crossover probability, mutation probability, population size, and maximum number of generations is also performed to determine the most befitting algorithm parameters. The results demonstrate that the application of genetic algorithm is able to mimic the key features of the flow and water quality process and that the calibration of models is efficient and robust. For the entire collection see [Zbl 1014.00019].

68T20AI problem solving (heuristics, search strategies, etc.)
Full Text: Link