Understanding the effects of subsidence on unconfined aquifer parameters by integration of Lattice Boltzmann Method (LBM) and Genetic Algorithm (GA)

Roghayeh Yousefi (), Nasser Talebbeydokhti, Seyyed Hosein Afzali, Maryam Dehghani and Ali Akbar Hekmatzadeh
Additional contact information
Roghayeh Yousefi: Shiraz University
Nasser Talebbeydokhti: Shiraz University
Seyyed Hosein Afzali: Shiraz University
Maryam Dehghani: Shiraz University
Ali Akbar Hekmatzadeh: Shiraz University of Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 115, issue 2, No 24, 1600 pages

Abstract: Abstract Excessive exploitation of groundwater has hitherto led to a significant land subsidence in a considerable number of plains in Iran. The compaction of aquifer layers ends up with changes in aquifer parameters, including hydraulic conductivity (Kx), specific yield (Sy), and compressibility (α). Accordingly, a precise estimation of aquifer parameters, Kx, Sy, and α seems essential for future water resources planning and management. In this study, an innovative inversion solution based on the combination of lattice Boltzmann method (LBM) and genetic algorithm (GA) was developed to determine the aquifer parameters, Kx, Sy, and α in Darab plain (in Fars province, Iran), which is highly subject to land subsidence. Herein, a newly developed LBM for unconfined groundwater flow was employed by incorporating the amount of subsidence measured by synthetic aperture radar interferometry (InSAR) spanning from 2010 to 2016. In order to optimize the aquifer parameters, the whole process of inverse modeling is replicated on the annual basis from 2010 to 2016 which leads to the temporal estimation of the aquifer parameters. Due to the compaction occurring in the aquifer system, a declining temporal trend is observed in the aquifer parameters in most parts of the plain. By fitting a function to time-dependent aquifer parameters, Kx, Sy, and α, their corresponding values and consequently the amount of subsidence in the near future, i.e., 2017, are predicted. The small average relative error (~ 3.5%) between the predicted land subsidence and the InSAR measurements demonstrates the high performance of the proposed inverse modeling approach. Graphical abstract

Keywords: Unconfined aquifer parameters; Subsidence; Lattice Boltzmann method; Genetic algorithm; Inverse solution; Groundwater flow (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-022-05607-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:115:y:2023:i:2:d:10.1007_s11069-022-05607-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-022-05607-1

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().