Groundwater well optimization to minimize contaminant movement from a surficial shallow aquifer to a lower water supply aquifer using stochastic simulation-optimization modeling techniques: Strategy formulation

The interaction between surficial shallow aquifers of poorer quality and semi-confined water-supply aquifers poses a potential risk for degradation of the water supply. Groundwater engineers and hydrogeologists use groundwater models to synthesize field data, conceptualize hydrological processes, an...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	MethodsX Ročník 9; s. 101765
Hlavní autoři:	Paul, Sondipon, Waldron, Brian, Jazaei, Farhad, Larsen, Daniel, Schoefernacker, Scott
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.01.2022 Elsevier
Témata:	Aquifer interaction aquifers computer software decision making decision support systems drawdown FloPy Genetic algorithm groundwater Groundwater contamination groundwater flow Groundwater model hydrologic models Method MODFLOW MODPATH Python risk saltwater intrusion simulation models Simulation-optimization model water pollution water supply Groundwater contamination Groundwater production well optimization methods Aquifer interaction MODPATH Genetic algorithm Groundwater model MODFLOW Simulation-optimization model FloPy Python
ISSN:	2215-0161, 2215-0161
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	The interaction between surficial shallow aquifers of poorer quality and semi-confined water-supply aquifers poses a potential risk for degradation of the water supply. Groundwater engineers and hydrogeologists use groundwater models to synthesize field data, conceptualize hydrological processes, and improve understanding of the groundwater system to support informed decision-making. Models for decision-making, called management models, aid in the efficient planning and sustainable management of groundwater systems. Management models search for the best or least-cost management strategy satisfying hydrologic and environmental regulations. In management models, a simulation model is linked or coupled with an optimization formulation. Widely used optimization formulations are linear, non-linear, quadratic, dynamic, and global search models. Management models are applied but are not limited to maximizing withdrawals, minimizing drawdown, pumping costs, and saltwater intrusion, and determining the best locations for production wells. This paper theoretically presents the development of groundwater wellfield management strategies and the corresponding modeling framework for each strategy's evaluation. Depending on the strategy, the modeling effort applies deterministic (simulation) and stochastic (simulation-optimization) techniques. The goals of the optimization strategies are to protect wells from potential contaminant sources, identify optimal future well installation sites, mitigate risks, and extend the life of wells that may face water contamination issues.•Several management strategies are formulated addressing well depth, seasonal pumping operation, and mapping no-drilling or red zones for new well installation.•Modeling methodologies are laid down that apply thousands of numerical simulations for each strategy to simulate and evaluate recurring patterns of contaminant movement.•The simulation model integrates MODFLOW and MODPATH to simulate 3D groundwater flow and advective contaminant movement, respectively and is transferred via FloPy to couple with the optimization/decision model using a custom Python script. [Display omitted]
AbstractList	The interaction between surficial shallow aquifers of poorer quality and semi-confined water-supply aquifers poses a potential risk for degradation of the water supply. Groundwater engineers and hydrogeologists use groundwater models to synthesize field data, conceptualize hydrological processes, and improve understanding of the groundwater system to support informed decision-making. Models for decision-making, called management models, aid in the efficient planning and sustainable management of groundwater systems. Management models search for the best or least-cost management strategy satisfying hydrologic and environmental regulations. In management models, a simulation model is linked or coupled with an optimization formulation. Widely used optimization formulations are linear, non-linear, quadratic, dynamic, and global search models. Management models are applied but are not limited to maximizing withdrawals, minimizing drawdown, pumping costs, and saltwater intrusion, and determining the best locations for production wells. This paper theoretically presents the development of groundwater wellfield management strategies and the corresponding modeling framework for each strategy's evaluation. Depending on the strategy, the modeling effort applies deterministic (simulation) and stochastic (simulation-optimization) techniques. The goals of the optimization strategies are to protect wells from potential contaminant sources, identify optimal future well installation sites, mitigate risks, and extend the life of wells that may face water contamination issues.•Several management strategies are formulated addressing well depth, seasonal pumping operation, and mapping no-drilling or red zones for new well installation.•Modeling methodologies are laid down that apply thousands of numerical simulations for each strategy to simulate and evaluate recurring patterns of contaminant movement.•The simulation model integrates MODFLOW and MODPATH to simulate 3D groundwater flow and advective contaminant movement, respectively and is transferred via FloPy to couple with the optimization/decision model using a custom Python script. The interaction between surficial shallow aquifers of poorer quality and semi-confined water-supply aquifers poses a potential risk for degradation of the water supply. Groundwater engineers and hydrogeologists use groundwater models to synthesize field data, conceptualize hydrological processes, and improve understanding of the groundwater system to support informed decision-making. Models for decision-making, called management models, aid in the efficient planning and sustainable management of groundwater systems. Management models search for the best or least-cost management strategy satisfying hydrologic and environmental regulations. In management models, a simulation model is linked or coupled with an optimization formulation. Widely used optimization formulations are linear, non-linear, quadratic, dynamic, and global search models. Management models are applied but are not limited to maximizing withdrawals, minimizing drawdown, pumping costs, and saltwater intrusion, and determining the best locations for production wells. This paper theoretically presents the development of groundwater wellfield management strategies and the corresponding modeling framework for each strategy's evaluation. Depending on the strategy, the modeling effort applies deterministic (simulation) and stochastic (simulation-optimization) techniques. The goals of the optimization strategies are to protect wells from potential contaminant sources, identify optimal future well installation sites, mitigate risks, and extend the life of wells that may face water contamination issues.•Several management strategies are formulated addressing well depth, seasonal pumping operation, and mapping no-drilling or red zones for new well installation.•Modeling methodologies are laid down that apply thousands of numerical simulations for each strategy to simulate and evaluate recurring patterns of contaminant movement.•The simulation model integrates MODFLOW and MODPATH to simulate 3D groundwater flow and advective contaminant movement, respectively and is transferred via FloPy to couple with the optimization/decision model using a custom Python script. [Display omitted] The interaction between surficial shallow aquifers of poorer quality and semi-confined water-supply aquifers poses a potential risk for degradation of the water supply. Groundwater engineers and hydrogeologists use groundwater models to synthesize field data, conceptualize hydrological processes, and improve understanding of the groundwater system to support informed decision-making. Models for decision-making, called management models, aid in the efficient planning and sustainable management of groundwater systems. Management models search for the best or least-cost management strategy satisfying hydrologic and environmental regulations. In management models, a simulation model is linked or coupled with an optimization formulation. Widely used optimization formulations are linear, non-linear, quadratic, dynamic, and global search models. Management models are applied but are not limited to maximizing withdrawals, minimizing drawdown, pumping costs, and saltwater intrusion, and determining the best locations for production wells. This paper theoretically presents the development of groundwater wellfield management strategies and the corresponding modeling framework for each strategy's evaluation. Depending on the strategy, the modeling effort applies deterministic (simulation) and stochastic (simulation-optimization) techniques. The goals of the optimization strategies are to protect wells from potential contaminant sources, identify optimal future well installation sites, mitigate risks, and extend the life of wells that may face water contamination issues.•Several management strategies are formulated addressing well depth, seasonal pumping operation, and mapping no-drilling or red zones for new well installation.•Modeling methodologies are laid down that apply thousands of numerical simulations for each strategy to simulate and evaluate recurring patterns of contaminant movement.•The simulation model integrates MODFLOW and MODPATH to simulate 3D groundwater flow and advective contaminant movement, respectively and is transferred via FloPy to couple with the optimization/decision model using a custom Python script. Image, graphical abstract The interaction between surficial shallow aquifers of poorer quality and semi-confined water-supply aquifers poses a potential risk for degradation of the water supply. Groundwater engineers and hydrogeologists use groundwater models to synthesize field data, conceptualize hydrological processes, and improve understanding of the groundwater system to support informed decision-making. Models for decision-making, called management models, aid in the efficient planning and sustainable management of groundwater systems. Management models search for the best or least-cost management strategy satisfying hydrologic and environmental regulations. In management models, a simulation model is linked or coupled with an optimization formulation. Widely used optimization formulations are linear, non-linear, quadratic, dynamic, and global search models. Management models are applied but are not limited to maximizing withdrawals, minimizing drawdown, pumping costs, and saltwater intrusion, and determining the best locations for production wells. This paper theoretically presents the development of groundwater wellfield management strategies and the corresponding modeling framework for each strategy's evaluation. Depending on the strategy, the modeling effort applies deterministic (simulation) and stochastic (simulation-optimization) techniques. The goals of the optimization strategies are to protect wells from potential contaminant sources, identify optimal future well installation sites, mitigate risks, and extend the life of wells that may face water contamination issues.•Several management strategies are formulated addressing well depth, seasonal pumping operation, and mapping no-drilling or red zones for new well installation.•Modeling methodologies are laid down that apply thousands of numerical simulations for each strategy to simulate and evaluate recurring patterns of contaminant movement.•The simulation model integrates MODFLOW and MODPATH to simulate 3D groundwater flow and advective contaminant movement, respectively and is transferred via FloPy to couple with the optimization/decision model using a custom Python script.The interaction between surficial shallow aquifers of poorer quality and semi-confined water-supply aquifers poses a potential risk for degradation of the water supply. Groundwater engineers and hydrogeologists use groundwater models to synthesize field data, conceptualize hydrological processes, and improve understanding of the groundwater system to support informed decision-making. Models for decision-making, called management models, aid in the efficient planning and sustainable management of groundwater systems. Management models search for the best or least-cost management strategy satisfying hydrologic and environmental regulations. In management models, a simulation model is linked or coupled with an optimization formulation. Widely used optimization formulations are linear, non-linear, quadratic, dynamic, and global search models. Management models are applied but are not limited to maximizing withdrawals, minimizing drawdown, pumping costs, and saltwater intrusion, and determining the best locations for production wells. This paper theoretically presents the development of groundwater wellfield management strategies and the corresponding modeling framework for each strategy's evaluation. Depending on the strategy, the modeling effort applies deterministic (simulation) and stochastic (simulation-optimization) techniques. The goals of the optimization strategies are to protect wells from potential contaminant sources, identify optimal future well installation sites, mitigate risks, and extend the life of wells that may face water contamination issues.•Several management strategies are formulated addressing well depth, seasonal pumping operation, and mapping no-drilling or red zones for new well installation.•Modeling methodologies are laid down that apply thousands of numerical simulations for each strategy to simulate and evaluate recurring patterns of contaminant movement.•The simulation model integrates MODFLOW and MODPATH to simulate 3D groundwater flow and advective contaminant movement, respectively and is transferred via FloPy to couple with the optimization/decision model using a custom Python script.
ArticleNumber	101765
Author	Waldron, Brian Jazaei, Farhad Paul, Sondipon Larsen, Daniel Schoefernacker, Scott
Author_xml	– sequence: 1 givenname: Sondipon orcidid: 0000-0002-3706-8055 surname: Paul fullname: Paul, Sondipon email: sondiponpaul@gmail.com organization: Department of Civil Engineering, University of Memphis, Memphis, TN 38152, USA – sequence: 2 givenname: Brian surname: Waldron fullname: Waldron, Brian organization: Department of Civil Engineering, University of Memphis, Memphis, TN 38152, USA – sequence: 3 givenname: Farhad surname: Jazaei fullname: Jazaei, Farhad organization: Department of Civil Engineering, University of Memphis, Memphis, TN 38152, USA – sequence: 4 givenname: Daniel surname: Larsen fullname: Larsen, Daniel organization: Department of Earth Sciences, University of Memphis, Memphis, TN 38152, USA – sequence: 5 givenname: Scott surname: Schoefernacker fullname: Schoefernacker, Scott organization: Center for Applied Earth Sciences and Engineering Research (CAESER), University of Memphis, Memphis, TN 38152, USA
BookMark	eNqFks1uEzEQx1eoiJbSB-DmI5cEf6ztXZCQUAWlUiUOwNmaeu3E0a6d2t6k4TF5IrxJiwiHcrDs-fj_xh7Py-rEB2-q6jXBc4KJeLuaD-Z-TjGlky0Ff1adUUr4rATJyV_n0-oipRXGmLCakZq-qE4Zbwgjoj6rfl3FMPpuC9lEtDV9j8I6u8H9hOyCRzmgwfnJNkgHn6FY4DMawsYMphxsDAMClMZonXbQo7SEvg9bBHejs4VZCICKY8Lvi6Rxve53f-Jjcn6BUg56CSk7jZIbxn5ffXZ0lSF0pp9ys9FL7-5Gk96hbzkW6GKHbIiPslfVcwt9MhcP-3n14_On75dfZjdfr64vP97MNKcyzyTmRtQCt1ILfAvMApMAQgAjxHIpWmYla00N2nJrbSdt8ba0Lkti0QA7r64P3C7ASq2jGyDuVACn9o4QFwpieVFvFBWdrC1ua9awmlPbctw00lgDrGtZIwrrw4G1Hm8H0-nS2gj9EfQ44t1SLcJGtZTLmkyANw-AGKbWZDW4pMt_gjdhTIpK0lDBGGX_TxVNg5uWC15S5SFVx5BSNFZpl_dNLpdwvSJYTbOoVqrMoppmUR1msSjJP8rHtzyleX_QmPJrG2eiStoZr03notG5tNU9of4NgOb-1Q
CitedBy_id	crossref_primary_10_1061_JHYEFF_HEENG_5829 crossref_primary_10_1007_s11269_023_03580_3 crossref_primary_10_1111_1752_1688_13150 crossref_primary_10_3390_w15020365 crossref_primary_10_1016_j_camwa_2024_10_033 crossref_primary_10_1016_j_gsd_2024_101172 crossref_primary_10_1002_clen_202300471
Cites_doi	10.1111/j.1745-6584.1990.tb01989.x 10.1046/j.1440-0952.2002.00936.x 10.1029/95RG00394 10.1016/j.jhydrol.2008.05.003 10.1007/s10040-015-1260-3 10.1061/(ASCE)0733-9496(2002)128:6(431) 10.1029/94WR00554 10.25125/engineering-journal-IJOER-MAY-2017-24 10.1016/j.jhydrol.2012.08.004 10.1007/s10040-008-0400-4 10.1016/S0883-2927(02)00204-4 10.1139/t96-080-302 10.1007/s10040-020-02141-x 10.1111/gwat.12269 10.1016/j.gsf.2011.03.003 10.1029/97WR00366 10.1111/gwat.12413 10.1029/93WR01494 10.1111/j.1745-6584.1999.tb01193.x
ContentType	Journal Article
Copyright	2022 2022 The Authors. Published by Elsevier B.V. 2022 The Authors. Published by Elsevier B.V. 2022
Copyright_xml	– notice: 2022 – notice: 2022 The Authors. Published by Elsevier B.V. – notice: 2022 The Authors. Published by Elsevier B.V. 2022
DBID	6I. AAFTH AAYXX CITATION 7X8 7S9 L.6 5PM DOA
DOI	10.1016/j.mex.2022.101765
DatabaseName	ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef MEDLINE - Academic AGRICOLA AGRICOLA - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef MEDLINE - Academic AGRICOLA AGRICOLA - Academic
DatabaseTitleList	AGRICOLA MEDLINE - Academic
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
EISSN	2215-0161
ExternalDocumentID	oai_doaj_org_article_26d74f094383452f950887efea3d9386 PMC9257416 10_1016_j_mex_2022_101765 S2215016122001455
GroupedDBID	0R~ 4.4 457 53G 5VS 6I. AACTN AAEDT AAEDW AAFTH AAHBH AAIKJ AALRI AAXUO ABMAC ACGFS ADBBV ADEZE ADRAZ ADVLN AEXQZ AFJKZ AFPKN AFTJW AGHFR AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ AOIJS BCNDV EBS EJD FDB GROUPED_DOAJ HYE IPNFZ IXB KQ8 M48 M~E OK1 RIG ROL RPM SSZ AAFWJ AAYWO AAYXX ACVFH ADCNI AEUPX AFPUW AIGII AKBMS AKYEP APXCP CITATION 7X8 7S9 L.6 5PM
ID	FETCH-LOGICAL-c527t-705e646097c60ba3fa37aa66a311f57693f739e4acf5fffd7ff579247927068a3
IEDL.DBID	DOA
ISICitedReferencesCount	7
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000829319700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2215-0161
IngestDate	Fri Oct 03 12:38:57 EDT 2025 Tue Sep 30 16:43:50 EDT 2025 Thu Jul 10 23:35:37 EDT 2025 Fri Jul 11 11:59:11 EDT 2025 Tue Nov 18 22:11:54 EST 2025 Thu Nov 13 04:20:20 EST 2025 Sun Apr 06 06:53:51 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Groundwater contamination Groundwater production well optimization methods Aquifer interaction MODPATH Genetic algorithm Groundwater model MODFLOW Simulation-optimization model FloPy Python
Language	English
License	This is an open access article under the CC BY license. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c527t-705e646097c60ba3fa37aa66a311f57693f739e4acf5fffd7ff579247927068a3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0002-3706-8055
OpenAccessLink	https://doaj.org/article/26d74f094383452f950887efea3d9386
PMID	35813164
PQID	2688089565
PQPubID	23479
ParticipantIDs	doaj_primary_oai_doaj_org_article_26d74f094383452f950887efea3d9386 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9257416 proquest_miscellaneous_2718263323 proquest_miscellaneous_2688089565 crossref_citationtrail_10_1016_j_mex_2022_101765 crossref_primary_10_1016_j_mex_2022_101765 elsevier_sciencedirect_doi_10_1016_j_mex_2022_101765
PublicationCentury	2000
PublicationDate	2022-01-01
PublicationDateYYYYMMDD	2022-01-01
PublicationDate_xml	– month: 01 year: 2022 text: 2022-01-01 day: 01
PublicationDecade	2020
PublicationTitle	MethodsX
PublicationYear	2022
Publisher	Elsevier B.V Elsevier
Publisher_xml	– name: Elsevier B.V – name: Elsevier
References	Larsen, Gentry, Solomon (bib0014) 2003; 18 Tamer Ayvaz, Karahan (bib0026) 2008; 357 Huang, Mayer (bib0010) 1997; 33 Gerber, Howard (bib0008) 1996; 33 Khalaf, Gad (bib0012) 2017; 3 McKinney, Lin (bib0017) 1994; 30 Parks, Mirecki (bib0021) 1992 Bakker (bib0004) 2014; 52 Kontos, Katsifarakis (bib0013) 2017; 14 Maskey, Jonoski, Solomatine (bib0016) 2002; 128 Wagner (bib0028) 1995; 33 Timms, Acworth (bib0027) 2002; 49 Zhou, Li (bib0032) 2011; 2 Goldberg (bib0009) 1989 Singh (bib0024) 2012; 466–467 Yeh (bib0031) 2015; 23 Pollock (bib0022) 2016 Rogers, Dowla (bib0023) 1994; 30 Parks (bib0020) 1990 Waldron, Harris, Larsen, Pell (bib0029) 2009; 17 Madsen, Perry (bib0015) 2010; 15 Niswonger, Panday, Ibaraki (bib0019) 2011 Anderson, Woessner, Hunt (bib0002) 2015 Freeze, Massmann, Smith, Sperling, James (bib0007) 1990; 28 Freeze, Gorelick (bib0006) 1999; 37 Bakker, Post, Langevin, Hughes, White, Starn, Fienen (bib0003) 2016; 54 Jazaei, Waldron, Schoefernacker, Larsen (bib0011) 2018; 11 Ahlfeld, Mulligan (bib0001) 2000 USEPA. Magnificent ground water connection. Grades 7-12: groundwater contamination – getting up to speed, United States Environmental Protection Agency, 2022 HDR 2019. Part 2. The MLGW way….forward Cognac, Ronayne (bib0005) 2020; 28 Pollock (10.1016/j.mex.2022.101765_bib0022) 2016 Parks (10.1016/j.mex.2022.101765_bib0020) 1990 Wagner (10.1016/j.mex.2022.101765_bib0028) 1995; 33 Madsen (10.1016/j.mex.2022.101765_bib0015) 2010; 15 Singh (10.1016/j.mex.2022.101765_bib0024) 2012; 466–467 Gerber (10.1016/j.mex.2022.101765_bib0008) 1996; 33 Kontos (10.1016/j.mex.2022.101765_bib0013) 2017; 14 10.1016/j.mex.2022.101765_bib0025 Timms (10.1016/j.mex.2022.101765_bib0027) 2002; 49 Ahlfeld (10.1016/j.mex.2022.101765_bib0001) 2000 Bakker (10.1016/j.mex.2022.101765_bib0004) 2014; 52 Khalaf (10.1016/j.mex.2022.101765_bib0012) 2017; 3 Larsen (10.1016/j.mex.2022.101765_bib0014) 2003; 18 Niswonger (10.1016/j.mex.2022.101765_bib0019) 2011 Bakker (10.1016/j.mex.2022.101765_bib0003) 2016; 54 Jazaei (10.1016/j.mex.2022.101765_bib0011) 2018; 11 10.1016/j.mex.2022.101765_bib0018 Goldberg (10.1016/j.mex.2022.101765_bib0009) 1989 Tamer Ayvaz (10.1016/j.mex.2022.101765_bib0026) 2008; 357 Rogers (10.1016/j.mex.2022.101765_bib0023) 1994; 30 Waldron (10.1016/j.mex.2022.101765_bib0029) 2009; 17 Parks (10.1016/j.mex.2022.101765_bib0021) 1992 Freeze (10.1016/j.mex.2022.101765_bib0006) 1999; 37 Cognac (10.1016/j.mex.2022.101765_bib0005) 2020; 28 McKinney (10.1016/j.mex.2022.101765_bib0017) 1994; 30 Maskey (10.1016/j.mex.2022.101765_bib0016) 2002; 128 Zhou (10.1016/j.mex.2022.101765_bib0032) 2011; 2 Freeze (10.1016/j.mex.2022.101765_bib0007) 1990; 28 Huang (10.1016/j.mex.2022.101765_bib0010) 1997; 33 Yeh (10.1016/j.mex.2022.101765_bib0031) 2015; 23 Anderson (10.1016/j.mex.2022.101765_bib0002) 2015
References_xml	– volume: 49 start-page: 525 year: 2002 end-page: 537 ident: bib0027 article-title: Origin, lithology and weathering characteristics of upper tertiary-quaternary clay aquitard units on the lower Murrumbidgee alluvial fan publication-title: Aust. J. Earth Sci. – year: 2000 ident: bib0001 article-title: Optimal Management of Flow in Groundwater Systems: An Introduction to Combining Simulation Models and Optimization Methods – year: 2016 ident: bib0022 article-title: User Guide for MODPATH Version 7 - A Particle-Tracking Model for MODFLOW – year: 1992 ident: bib0021 article-title: Hydrogeology, Groundwater Quality, and Potential for Water-Supply Contamination Near the Shelby County Landfill in Memphis, Tennessee – volume: 33 start-page: 1021 year: 1995 end-page: 1028 ident: bib0028 article-title: Recent advances in simulation-optimization groundwater management modeling publication-title: Rev. Geophys. – volume: 128 start-page: 431 year: 2002 end-page: 440 ident: bib0016 article-title: Groundwater remediation strategy using global optimization algorithms publication-title: J. Water Resour. Plann. Manag. – volume: 28 start-page: 738 year: 1990 end-page: 766 ident: bib0007 article-title: Hydrogeological decision analysis: 1. A framework publication-title: Ground Water – volume: 466–467 start-page: 167 year: 2012 end-page: 182 ident: bib0024 article-title: An overview of the optimization modelling applications publication-title: J. Hydrol. – volume: 54 start-page: 733 year: 2016 end-page: 739 ident: bib0003 article-title: Scripting MODFLOW model development using Python and FloPy publication-title: Groundwater – volume: 3 start-page: 1 year: 2017 end-page: 18 ident: bib0012 article-title: Optimal well locations using genetic algorithm for Tushki project, western desert, Egypt publication-title: Int. J. Eng. Res. Sci. (IJOER) – year: 2015 ident: bib0002 article-title: Applied Groundwater Modeling — Simulation of Flow and Advective Transport – volume: 2 start-page: 205 year: 2011 end-page: 214 ident: bib0032 article-title: A review of regional groundwater flow modeling publication-title: Geosci. Front. – reference: USEPA. Magnificent ground water connection. Grades 7-12: groundwater contamination – getting up to speed, United States Environmental Protection Agency, 2022 – volume: 37 start-page: 934 year: 1999 end-page: 954 ident: bib0006 article-title: Convergence of stochastic optimization and decision analysis in the engineering design of aquifer remediation publication-title: Ground Water – volume: 14 start-page: 190 year: 2017 end-page: 200 ident: bib0013 article-title: Genetic algorithms in polluted coastal aquifers' management publication-title: WSEAS Trans. Inf. Sci. Appl. – volume: 33 start-page: 538 year: 1996 end-page: 555 ident: bib0008 article-title: Evidence for recent groundwater flow through late wisconsinan till near Toronto, Ontario publication-title: Can. Geotech. J. – volume: 52 start-page: 821 year: 2014 end-page: 822 ident: bib0004 article-title: Python scripting: the return to programming publication-title: Groundwater – reference: HDR 2019. Part 2. The MLGW way….forward – volume: 11 year: 2018 ident: bib0011 article-title: Application of numerical tools to investigate a leaky aquitard beneath urban well fields publication-title: Water – volume: 357 start-page: 76 year: 2008 end-page: 92 ident: bib0026 article-title: A simulation/optimization model for the identification of unknown groundwater well locations and pumping rates publication-title: J. Hydrol. – year: 1989 ident: bib0009 article-title: Genetic Algorithms in Search, Optimization, and Machine Learning – volume: 17 start-page: 505 year: 2009 end-page: 517 ident: bib0029 article-title: Mapping an aquitard breach using shear-wave seismic reflection publication-title: Hydrogeol. J. – volume: 15 start-page: 11 year: 2010 ident: bib0015 article-title: Using genetic algorithms on groundwater modeling problems in a consulting setting publication-title: Proceedings of the Annual International Conference on Soils, Sediments, Water and Energy – volume: 30 start-page: 1897 year: 1994 end-page: 1906 ident: bib0017 article-title: Genetic algorithm solution of groundwater management models publication-title: Water Resour. Res. – start-page: 44 year: 2011 ident: bib0019 article-title: MODFLOW-NWT, A Newton Formulation for MODFLOW-2005 – volume: 23 start-page: 1051 year: 2015 end-page: 1065 ident: bib0031 article-title: Review: Optimization methods for groundwater modeling and management publication-title: Hydrogeol. J. – volume: 30 start-page: 457 year: 1994 end-page: 481 ident: bib0023 article-title: Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling publication-title: Water Resour. Res. – volume: 18 start-page: 1043 year: 2003 end-page: 1063 ident: bib0014 article-title: The geochemistry and mixing of leakage in a semi-confined aquifer at a municipal well field, Memphis, Tennessee, USA publication-title: Appl. Geochem. – year: 1990 ident: bib0020 article-title: Hydrogeology and Preliminary Assessment of the Potential for Contamination of the Memphis Aquifer – volume: 33 start-page: 1001 year: 1997 end-page: 1012 ident: bib0010 article-title: Pump-and-treat optimization using well locations and pumping rates as decision variables publication-title: Water Resour. Res. – volume: 28 start-page: 1359 year: 2020 end-page: 1370 ident: bib0005 article-title: Changes to inter-aquifer exchange resulting from long-term pumping: implications for bedrock groundwater recharge publication-title: Hydrogeol. J. – year: 1992 ident: 10.1016/j.mex.2022.101765_bib0021 – year: 2015 ident: 10.1016/j.mex.2022.101765_bib0002 – volume: 28 start-page: 738 issue: 5 year: 1990 ident: 10.1016/j.mex.2022.101765_bib0007 article-title: Hydrogeological decision analysis: 1. A framework publication-title: Ground Water doi: 10.1111/j.1745-6584.1990.tb01989.x – volume: 15 start-page: 11 year: 2010 ident: 10.1016/j.mex.2022.101765_bib0015 article-title: Using genetic algorithms on groundwater modeling problems in a consulting setting – start-page: 44 year: 2011 ident: 10.1016/j.mex.2022.101765_bib0019 – volume: 49 start-page: 525 issue: 3 year: 2002 ident: 10.1016/j.mex.2022.101765_bib0027 article-title: Origin, lithology and weathering characteristics of upper tertiary-quaternary clay aquitard units on the lower Murrumbidgee alluvial fan publication-title: Aust. J. Earth Sci. doi: 10.1046/j.1440-0952.2002.00936.x – ident: 10.1016/j.mex.2022.101765_bib0018 – volume: 33 start-page: 1021 issue: S2 year: 1995 ident: 10.1016/j.mex.2022.101765_bib0028 article-title: Recent advances in simulation-optimization groundwater management modeling publication-title: Rev. Geophys. doi: 10.1029/95RG00394 – volume: 357 start-page: 76 issue: 1–2 year: 2008 ident: 10.1016/j.mex.2022.101765_bib0026 article-title: A simulation/optimization model for the identification of unknown groundwater well locations and pumping rates publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2008.05.003 – volume: 23 start-page: 1051 issue: 6 year: 2015 ident: 10.1016/j.mex.2022.101765_bib0031 article-title: Review: Optimization methods for groundwater modeling and management publication-title: Hydrogeol. J. doi: 10.1007/s10040-015-1260-3 – year: 2016 ident: 10.1016/j.mex.2022.101765_bib0022 – volume: 128 start-page: 431 issue: 6 year: 2002 ident: 10.1016/j.mex.2022.101765_bib0016 article-title: Groundwater remediation strategy using global optimization algorithms publication-title: J. Water Resour. Plann. Manag. doi: 10.1061/(ASCE)0733-9496(2002)128:6(431) – volume: 30 start-page: 1897 issue: 6 year: 1994 ident: 10.1016/j.mex.2022.101765_bib0017 article-title: Genetic algorithm solution of groundwater management models publication-title: Water Resour. Res. doi: 10.1029/94WR00554 – volume: 3 start-page: 1 issue: 7 year: 2017 ident: 10.1016/j.mex.2022.101765_bib0012 article-title: Optimal well locations using genetic algorithm for Tushki project, western desert, Egypt publication-title: Int. J. Eng. Res. Sci. (IJOER) doi: 10.25125/engineering-journal-IJOER-MAY-2017-24 – volume: 466–467 start-page: 167 year: 2012 ident: 10.1016/j.mex.2022.101765_bib0024 article-title: An overview of the optimization modelling applications publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2012.08.004 – volume: 17 start-page: 505 issue: 3 year: 2009 ident: 10.1016/j.mex.2022.101765_bib0029 article-title: Mapping an aquitard breach using shear-wave seismic reflection publication-title: Hydrogeol. J. doi: 10.1007/s10040-008-0400-4 – ident: 10.1016/j.mex.2022.101765_bib0025 – volume: 18 start-page: 1043 issue: 7 year: 2003 ident: 10.1016/j.mex.2022.101765_bib0014 article-title: The geochemistry and mixing of leakage in a semi-confined aquifer at a municipal well field, Memphis, Tennessee, USA publication-title: Appl. Geochem. doi: 10.1016/S0883-2927(02)00204-4 – year: 1990 ident: 10.1016/j.mex.2022.101765_bib0020 – volume: 33 start-page: 538 year: 1996 ident: 10.1016/j.mex.2022.101765_bib0008 article-title: Evidence for recent groundwater flow through late wisconsinan till near Toronto, Ontario publication-title: Can. Geotech. J. doi: 10.1139/t96-080-302 – volume: 14 start-page: 190 year: 2017 ident: 10.1016/j.mex.2022.101765_bib0013 article-title: Genetic algorithms in polluted coastal aquifers' management publication-title: WSEAS Trans. Inf. Sci. Appl. – volume: 28 start-page: 1359 issue: 4 year: 2020 ident: 10.1016/j.mex.2022.101765_bib0005 article-title: Changes to inter-aquifer exchange resulting from long-term pumping: implications for bedrock groundwater recharge publication-title: Hydrogeol. J. doi: 10.1007/s10040-020-02141-x – volume: 11 issue: 5 year: 2018 ident: 10.1016/j.mex.2022.101765_bib0011 article-title: Application of numerical tools to investigate a leaky aquitard beneath urban well fields publication-title: Water – volume: 52 start-page: 821 issue: 6 year: 2014 ident: 10.1016/j.mex.2022.101765_bib0004 article-title: Python scripting: the return to programming publication-title: Groundwater doi: 10.1111/gwat.12269 – year: 2000 ident: 10.1016/j.mex.2022.101765_bib0001 – year: 1989 ident: 10.1016/j.mex.2022.101765_bib0009 – volume: 2 start-page: 205 issue: 2 year: 2011 ident: 10.1016/j.mex.2022.101765_bib0032 article-title: A review of regional groundwater flow modeling publication-title: Geosci. Front. doi: 10.1016/j.gsf.2011.03.003 – volume: 33 start-page: 1001 issue: 5 year: 1997 ident: 10.1016/j.mex.2022.101765_bib0010 article-title: Pump-and-treat optimization using well locations and pumping rates as decision variables publication-title: Water Resour. Res. doi: 10.1029/97WR00366 – volume: 54 start-page: 733 issue: 5 year: 2016 ident: 10.1016/j.mex.2022.101765_bib0003 article-title: Scripting MODFLOW model development using Python and FloPy publication-title: Groundwater doi: 10.1111/gwat.12413 – volume: 30 start-page: 457 issue: 2 year: 1994 ident: 10.1016/j.mex.2022.101765_bib0023 article-title: Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling publication-title: Water Resour. Res. doi: 10.1029/93WR01494 – volume: 37 start-page: 934 issue: 6 year: 1999 ident: 10.1016/j.mex.2022.101765_bib0006 article-title: Convergence of stochastic optimization and decision analysis in the engineering design of aquifer remediation publication-title: Ground Water doi: 10.1111/j.1745-6584.1999.tb01193.x
SSID	ssj0001343142
Score	2.2465045
Snippet	The interaction between surficial shallow aquifers of poorer quality and semi-confined water-supply aquifers poses a potential risk for degradation of the...
SourceID	doaj pubmedcentral proquest crossref elsevier
SourceType	Open Website Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	101765
SubjectTerms	Aquifer interaction aquifers computer software decision making decision support systems drawdown FloPy Genetic algorithm groundwater Groundwater contamination groundwater flow Groundwater model hydrologic models Method MODFLOW MODPATH Python risk saltwater intrusion simulation models Simulation-optimization model water pollution water supply
Title	Groundwater well optimization to minimize contaminant movement from a surficial shallow aquifer to a lower water supply aquifer using stochastic simulation-optimization modeling techniques: Strategy formulation
URI	https://dx.doi.org/10.1016/j.mex.2022.101765 https://www.proquest.com/docview/2688089565 https://www.proquest.com/docview/2718263323 https://pubmed.ncbi.nlm.nih.gov/PMC9257416 https://doaj.org/article/26d74f094383452f950887efea3d9386
Volume	9
WOSCitedRecordID	wos000829319700004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2215-0161 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001343142 issn: 2215-0161 databaseCode: DOA dateStart: 20140101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2215-0161 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001343142 issn: 2215-0161 databaseCode: M~E dateStart: 20140101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Nj5QwFG90Y4wX42ccPzbPxJMJESjQ4k3NbrzsxoMmcyOlH-6YHdCBcd2Lf6R_ke-1nQlcxosHSIDSUvro--jj92PslRZ1pnPVJpWV6KDwVCaqLWUijWqNdg51mvFkE-L8XC6X9acJ1RflhAV44PDi3uSVEYWj_DfJizJ3nrVUWGcVNzWXHmw7FfXEmfLRFY6K0TPn5KjTErJrdkuaPrlrbX-hb5jnHmaIFMtEKXns_plumtie88zJiSo6vcfuRhsS3oVnv89u2O4Bu30WV8kfsj8UUOrMFZqRG6DgHPQ4MazjH5cw9kCAInhsgTLVVciGgXXvscNHoF9OQMGw3QR4CRiIcaW_AvVjS5kwVIOCS-JXg9DIQNyg1_vrlEz_FdCs1BeKcKBhWK0jTVgyexTPw0Nl91Cyw1uIgLnXQPZ0vO0R-3J68vnDxyRyNyS6zMWYiLS0VVGltdBV2iruFBdKVZXiWeZKImB0gte2UNqVzjkjHJ5FXxA3kVZS8cfsqOs7-4SBE1lWGZ1JQ1jxuWhlQaD8MqUAjDN6wdLd4DU6ApsTv8Zls8tg-9bgeDc03k0Y7wV7vb_le0D1OFT4PUnEviABcvsTKKZNFNPmX2K6YMVOnppo2wSbBataHWr75U72GvzuaTFHdbbfDtggzrwSvdtDZQR5j5znfMHETHBnnZlf6VYXHmW8xskcrfWn_6P3z9gd6lQIXT1nR-Nma1-wW_rnuBo2x-ymWMpj_wHj_uz3yV8BWFAZ
linkProvider	Directory of Open Access Journals
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Groundwater+well+optimization+to+minimize+contaminant+movement+from+a+surficial+shallow+aquifer+to+a+lower+water+supply+aquifer+using+stochastic+simulation-optimization+modeling+techniques%3A+Strategy+formulation&rft.jtitle=MethodsX&rft.au=Paul%2C+Sondipon&rft.au=Waldron%2C+Brian&rft.au=Jazaei%2C+Farhad&rft.au=Larsen%2C+Daniel&rft.date=2022-01-01&rft.pub=Elsevier&rft.eissn=2215-0161&rft.volume=9&rft_id=info:doi/10.1016%2Fj.mex.2022.101765&rft_id=info%3Apmid%2F35813164&rft.externalDocID=PMC9257416
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2215-0161&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2215-0161&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2215-0161&client=summon