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Zheng Duan

Universitetslektor

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Advancing SWAT Model Calibration : A U-NSGA-III-Based Framework for Multi-Objective Optimization

Författare

  • Huihui Mao
  • Chen Wang
  • Yan He
  • Xianfeng Song
  • Run Ma
  • Runkui Li
  • Zheng Duan

Summary, in English

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.

Avdelning/ar

  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • MERGE: ModElling the Regional and Global Earth system
  • Institutionen för naturgeografi och ekosystemvetenskap

Publiceringsår

2024-11

Språk

Engelska

Publikation/Tidskrift/Serie

Water (Switzerland)

Volym

16

Issue

21

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Oceanography, Hydrology, Water Resources
  • Water Engineering

Nyckelord

  • multi-objective optimization
  • parallel processing
  • parameter calibration
  • sensitivity analyses
  • SWAT model
  • U-NSGA-III

Aktiv

Published

ISBN/ISSN/Övrigt

  • ISSN: 2073-4441