Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm

Improving the Performance of Hydrological Model Parameter Uncertainty Analysis Using a Constrained Multi-Objective Intelligent Optimization Algorithm

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce b...

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Bibliographic Details
Main Authors: Xichen Liu, Guangyuan Kan, Liuqian Ding, Xiaoyan He, Ronghua Liu, Ke Liang
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Water
Subjects:
uncertainty analysis
rainfall–runoff model
constrained NSGA-II multi-objective optimization
parameter physical meaning
population diversity
Online Access:https://www.mdpi.com/2073-4441/15/15/2700

Internet

https://www.mdpi.com/2073-4441/15/15/2700

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