Multi-objective parameter optimization of distributed hydrological models based on data-poor watersheds

Ke Xu; Kun Yang

doi:10.1117/12.2674764

23 May 2023 Multi-objective parameter optimization of distributed hydrological models based on data-poor watersheds

Ke Xu, Kun Yang

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126041J (2023) https://doi.org/10.1117/12.2674764
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

The distributed hydrological model's high-resolution representation of watershed heterogeneity is especially suitable for hydrological simulation in watersheds with small areas, and the hydrological model needs to use flow observation data for parameter calibration to obtain adapted parameters, but the lack of observation basins is the core problem we face in China, where there are few hydrological stations and they are concentrated in important rivers. This work attempts to optimize the parameters of the distributed hydrological model using limited flow observation data in a small watershed where flow observation is relatively scarce and to explore the value of combining multiple sources of data to optimize the parameters of the distributed hydrological model. Based on the combination of meteorological observations and remote sensing data collected, a distributed hydrological model is constructed based on the WetSpa model for the Jianshan River basin, and a multi-objective genetic algorithm NSGA-II is applied to rate the model to predict the simulated basin runoff process. The results show that the hydrological model optimized by multi-objective parameters has good adaptability in the study area, and the simulation has certain accuracy, which can provide basic support for the simulation of the water environment in the basin and also provide a reference for hydrological simulation.

Citation Download Citation

Ke Xu and Kun Yang "Multi-objective parameter optimization of distributed hydrological models based on data-poor watersheds", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126041J (23 May 2023); https://doi.org/10.1117/12.2674764

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Mathematical optimization

Calibration

Rain

Atmospheric modeling

Genetic algorithms

Genetics

Show All Keywords

Keywords/Phrases

Search In:

Publication Years