Extracting a Set of Robust Pareto-Optimal Parameters for Hydrologic Models using NSGA-II and SCEM

A. NAZEMI; Xin YAO; A.H. CHAN

doi:10.1109/CEC.2006.1688539

Extracting a Set of Robust Pareto-Optimal Parameters for Hydrologic Models using NSGA-II and SCEM

A. NAZEMI, Xin YAO, A.H. CHAN

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

Abstract

In this paper, we will present a heuristic method in order to combine the information about the parametric space of a conceptual hydrologic model from two different sources. On one hand, multi-objective evolutionary optimization algorithm NSGA-II is used to find a set of pareto optimal solutions. On the other hand, a Markov Chain Monte Carlo-based algorithm, i. e. Shuffled Complex Evolution Metropolis (SCEM) is used to highlight a set of parameters with higher posterior distribution. By covering the interval between the most crowded locations in the parametric space extracted by both algorithms, we will identify a set of pareto optimal solutions which is more robust than the initial non-dominated set extracted by only NSGA-II.

Original language	English
Title of host publication	2006 IEEE International Conference on Evolutionary Computation
Publisher	IEEE
Pages	1901-1908
Number of pages	8
ISBN (Print)	0780394879
DOIs	https://doi.org/10.1109/CEC.2006.1688539
Publication status	Published - 2006
Externally published	Yes
Event	2006 IEEE International Conference on Evolutionary Computation - Vancouver, Canada Duration: 16 Jul 2006 → 21 Jul 2006

Conference

Conference	2006 IEEE International Conference on Evolutionary Computation
Country/Territory	Canada
City	Vancouver
Period	16/07/06 → 21/07/06

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10.1109/CEC.2006.1688539

Cite this

@inproceedings{6f8c0c6f90f6457286ff388bf593667f,

title = "Extracting a Set of Robust Pareto-Optimal Parameters for Hydrologic Models using NSGA-II and SCEM",

abstract = "In this paper, we will present a heuristic method in order to combine the information about the parametric space of a conceptual hydrologic model from two different sources. On one hand, multi-objective evolutionary optimization algorithm NSGA-II is used to find a set of pareto optimal solutions. On the other hand, a Markov Chain Monte Carlo-based algorithm, i. e. Shuffled Complex Evolution Metropolis (SCEM) is used to highlight a set of parameters with higher posterior distribution. By covering the interval between the most crowded locations in the parametric space extracted by both algorithms, we will identify a set of pareto optimal solutions which is more robust than the initial non-dominated set extracted by only NSGA-II.",

author = "A. NAZEMI and Xin YAO and A.H. CHAN",

year = "2006",

doi = "10.1109/CEC.2006.1688539",

language = "English",

isbn = "0780394879",

pages = "1901--1908",

booktitle = "2006 IEEE International Conference on Evolutionary Computation",

publisher = "IEEE",

note = "2006 IEEE International Conference on Evolutionary Computation ; Conference date: 16-07-2006 Through 21-07-2006",

}

NAZEMI, A, YAO, X & CHAN, AH 2006, Extracting a Set of Robust Pareto-Optimal Parameters for Hydrologic Models using NSGA-II and SCEM. in 2006 IEEE International Conference on Evolutionary Computation. IEEE, pp. 1901-1908, 2006 IEEE International Conference on Evolutionary Computation, Vancouver, British Columbia, Canada, 16/07/06. https://doi.org/10.1109/CEC.2006.1688539

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AU - NAZEMI, A.

AU - YAO, Xin

AU - CHAN, A.H.

PY - 2006

Y1 - 2006

N2 - In this paper, we will present a heuristic method in order to combine the information about the parametric space of a conceptual hydrologic model from two different sources. On one hand, multi-objective evolutionary optimization algorithm NSGA-II is used to find a set of pareto optimal solutions. On the other hand, a Markov Chain Monte Carlo-based algorithm, i. e. Shuffled Complex Evolution Metropolis (SCEM) is used to highlight a set of parameters with higher posterior distribution. By covering the interval between the most crowded locations in the parametric space extracted by both algorithms, we will identify a set of pareto optimal solutions which is more robust than the initial non-dominated set extracted by only NSGA-II.

AB - In this paper, we will present a heuristic method in order to combine the information about the parametric space of a conceptual hydrologic model from two different sources. On one hand, multi-objective evolutionary optimization algorithm NSGA-II is used to find a set of pareto optimal solutions. On the other hand, a Markov Chain Monte Carlo-based algorithm, i. e. Shuffled Complex Evolution Metropolis (SCEM) is used to highlight a set of parameters with higher posterior distribution. By covering the interval between the most crowded locations in the parametric space extracted by both algorithms, we will identify a set of pareto optimal solutions which is more robust than the initial non-dominated set extracted by only NSGA-II.

U2 - 10.1109/CEC.2006.1688539

DO - 10.1109/CEC.2006.1688539

M3 - Conference paper (refereed)

SN - 0780394879

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EP - 1908

BT - 2006 IEEE International Conference on Evolutionary Computation

PB - IEEE

T2 - 2006 IEEE International Conference on Evolutionary Computation

Y2 - 16 July 2006 through 21 July 2006

ER -

Extracting a Set of Robust Pareto-Optimal Parameters for Hydrologic Models using NSGA-II and SCEM

Abstract

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