Multi-Objective Calibration of Nonlinear Muskingum Model Using Non-Dominated Sorting Genetic Algorithm-II
- 10.2991/amsm-16.2016.38How to use a DOI?
- muskingum model; multi-objective optimization; nsga-ii; parameter estimation
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.
- © 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/).
Cite this article
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 -