Advancing SWAT Model Calibration : A U-NSGA-III-Based Framework for Multi-Objective Optimization
(2024) In Water (Switzerland) 16(21).- Abstract
In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by... (More)
In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.
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- author
- Mao, Huihui ; Wang, Chen ; He, Yan ; Song, Xianfeng ; Ma, Run ; Li, Runkui and Duan, Zheng LU
- organization
- publishing date
- 2024-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- multi-objective optimization, parallel processing, parameter calibration, sensitivity analyses, SWAT model, U-NSGA-III
- in
- Water (Switzerland)
- volume
- 16
- issue
- 21
- article number
- 3030
- publisher
- MDPI AG
- external identifiers
-
- scopus:85208600293
- ISSN
- 2073-4441
- DOI
- 10.3390/w16213030
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2024 by the authors.
- id
- 226407f0-5677-4b49-aedf-c2b2e7db83b6
- date added to LUP
- 2025-01-15 10:46:13
- date last changed
- 2025-04-04 14:56:58
@article{226407f0-5677-4b49-aedf-c2b2e7db83b6,
abstract = {{<p>In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.</p>}},
author = {{Mao, Huihui and Wang, Chen and He, Yan and Song, Xianfeng and Ma, Run and Li, Runkui and Duan, Zheng}},
issn = {{2073-4441}},
keywords = {{multi-objective optimization; parallel processing; parameter calibration; sensitivity analyses; SWAT model; U-NSGA-III}},
language = {{eng}},
number = {{21}},
publisher = {{MDPI AG}},
series = {{Water (Switzerland)}},
title = {{Advancing SWAT Model Calibration : A U-NSGA-III-Based Framework for Multi-Objective Optimization}},
url = {{http://dx.doi.org/10.3390/w16213030}},
doi = {{10.3390/w16213030}},
volume = {{16}},
year = {{2024}},
}