Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments

Mohammadi, Babak LU orcid ; Vazifehkhah, Saeed and Duan, Zheng LU (2024) In Engineering Applications of Artificial Intelligence 127.
Abstract

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential for water resources management. The present study aims to enhance a conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven and swarm computing for enhancing the accuracy of rainfall runoff prediction. The proposed framework combines the conceptual hydrological model (i.e. GSM) with the time series predictor model (SVR) and optimization-driven parameter tuning of the firefly algorithm (SVR-FFA). This integration uniquely captures the complex interplay between meteorological variables, glacier processes, and hydrological responses. Applying the hybrid framework proved better results than the... (More)

Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential for water resources management. The present study aims to enhance a conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven and swarm computing for enhancing the accuracy of rainfall runoff prediction. The proposed framework combines the conceptual hydrological model (i.e. GSM) with the time series predictor model (SVR) and optimization-driven parameter tuning of the firefly algorithm (SVR-FFA). This integration uniquely captures the complex interplay between meteorological variables, glacier processes, and hydrological responses. Applying the hybrid framework proved better results than the standalone GSM and ordinary SVR in simulating runoff time series. The performance of the proposed conceptual integrated metaheuristic-based framework (W-SG-SVR-FFA) demonstrated several enhancements over the standalone GSM model. During the calibration (validation) period, the evaluation metric coefficient of determination (R2) was 0.77 (0.77) for the standalone GSM model and 0.98 (0.91) for the W-SG-SVR-FFA model. The Kling-Gupta Efficiency (KGE) values were 0.81 (0.77) and 0.97 (0.87), respectively. Applying the method in glacierized catchments underscores its importance in areas undergoing swift climate change and glacial melting. This approach enables readers to witness the intricate equilibrium between the model's complexity and the accuracy of simulation outcomes.

(Less)
Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Data-driven modeling, Evolutionary computation, Hydrological modeling, Metaheuristic optimization, Runoff simulation, Time series analysis
in
Engineering Applications of Artificial Intelligence
volume
127
article number
107302
publisher
Engineering Applications of Artificial Intelligence
external identifiers
  • scopus:85176094244
ISSN
0952-1976
DOI
10.1016/j.engappai.2023.107302
language
English
LU publication?
yes
id
0148d701-dd1d-4504-b382-ac8d9ae1101f
date added to LUP
2023-11-24 14:06:27
date last changed
2023-11-24 16:58:35
@article{0148d701-dd1d-4504-b382-ac8d9ae1101f,
  abstract     = {{<p>Glacio-hydrological modeling is a key task for assessing the influence of snow and glaciers on water resources, essential for water resources management. The present study aims to enhance a conceptual hydrological model (namely Glacial Snow Melt (GSM)) by data-driven and swarm computing for enhancing the accuracy of rainfall runoff prediction. The proposed framework combines the conceptual hydrological model (i.e. GSM) with the time series predictor model (SVR) and optimization-driven parameter tuning of the firefly algorithm (SVR-FFA). This integration uniquely captures the complex interplay between meteorological variables, glacier processes, and hydrological responses. Applying the hybrid framework proved better results than the standalone GSM and ordinary SVR in simulating runoff time series. The performance of the proposed conceptual integrated metaheuristic-based framework (W-SG-SVR-FFA) demonstrated several enhancements over the standalone GSM model. During the calibration (validation) period, the evaluation metric coefficient of determination (R<sup>2</sup>) was 0.77 (0.77) for the standalone GSM model and 0.98 (0.91) for the W-SG-SVR-FFA model. The Kling-Gupta Efficiency (KGE) values were 0.81 (0.77) and 0.97 (0.87), respectively. Applying the method in glacierized catchments underscores its importance in areas undergoing swift climate change and glacial melting. This approach enables readers to witness the intricate equilibrium between the model's complexity and the accuracy of simulation outcomes.</p>}},
  author       = {{Mohammadi, Babak and Vazifehkhah, Saeed and Duan, Zheng}},
  issn         = {{0952-1976}},
  keywords     = {{Data-driven modeling; Evolutionary computation; Hydrological modeling; Metaheuristic optimization; Runoff simulation; Time series analysis}},
  language     = {{eng}},
  publisher    = {{Engineering Applications of Artificial Intelligence}},
  series       = {{Engineering Applications of Artificial Intelligence}},
  title        = {{A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments}},
  url          = {{http://dx.doi.org/10.1016/j.engappai.2023.107302}},
  doi          = {{10.1016/j.engappai.2023.107302}},
  volume       = {{127}},
  year         = {{2024}},
}