Multi-Objective Calibration of Nonlinear Muskingum Model Using Non-Dominated Sorting Genetic Algorithm-II

DOI

10.2991/amsm-16.2016.38How to use a DOI?

Keywords

muskingum model; multi-objective optimization; nsga-ii; parameter estimation

Abstract

Parameter calibration of hydrological model is one of the most important issues in the field of hydrology. Practice experience suggests that the traditional calibration of hydrological model with single objective cannot properly measure all of the behaviors of hydrological system. In order to successfully calibrate a hydrological model, multiple criteria should be considered. In this study, an multi-objective calibration routine of Muskingum model is developed using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The performance of the multi-objective calibration procedure is authenticated by three cases involving single-peak, multi-peak, and non-smooth hydrographs. The results show that the multi-objective calibration procedure is consistent and effective in estimating parameters of the Muskingum model.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Jungang Luo
AU  - Xiao Zhang
AU  - Xuan Zhang
PY  - 2016/05
DA  - 2016/05
TI  - Multi-Objective Calibration of Nonlinear Muskingum Model Using Non-Dominated Sorting Genetic Algorithm-II
BT  - Proceedings of the 2016 International Conference on Applied Mathematics, Simulation and Modelling
PB  - Atlantis Press
SP  - 165
EP  - 170
SN  - 2352-538X
UR  - https://doi.org/10.2991/amsm-16.2016.38
DO  - 10.2991/amsm-16.2016.38
ID  - Luo2016/05
ER  -