NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh

Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous input...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of Groundwater Science and Engineering Ročník 8; číslo 2; s. 118
Hlavní autoři: Abdullah Al Jami, Meher Uddin Himel, Hasan, Khairul, Basak, Shilpy Rani, Ayesha Ferdous Mita
Médium: Journal Article
Jazyk:angličtina
Vydáno: Shijiazhuang 2020
ISSN:2305-7068
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs (NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error (MSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient of efficiency (NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.
AbstractList Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater level is necessary for jointly planning the uses of groundwater resources. In this study, a new nonlinear autoregressive with exogenous inputs (NARX) network has been applied to simulate monthly groundwater levels in a well of Sylhet Sadar at a local scale. The Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms were used to train the NARX network, and the results were compared to determine the best architecture for predicting monthly groundwater levels over time. The comparison between LM and BR showed that NARX-BR has advantages over predicting monthly levels based on the Mean Squared Error (MSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient of efficiency (NSE). The results show that BR is the most accurate method for predicting groundwater levels with an error of ± 0.35 m. This method is applied to the management of irrigation water source, which provides important information for the prediction of local groundwater fluctuation at local level during a short period.
Author Ayesha Ferdous Mita
Basak, Shilpy Rani
Meher Uddin Himel
Abdullah Al Jami
Hasan, Khairul
Author_xml – sequence: 1
  fullname: Abdullah Al Jami
– sequence: 2
  fullname: Meher Uddin Himel
– sequence: 3
  givenname: Khairul
  surname: Hasan
  fullname: Hasan, Khairul
– sequence: 4
  givenname: Shilpy
  surname: Basak
  middlename: Rani
  fullname: Basak, Shilpy Rani
– sequence: 5
  fullname: Ayesha Ferdous Mita
BookMark eNo9j11LwzAYhXMxwan7DwHxztY3yZqkl3P4BUPBKXg3suTt2q0mM00d-_cWFK8eOBfnOeeMjHzwSMgVg5yVUqibbW79rsm5gCJTIHXOgUMOPAcQIzL-z0_JpOu2AMB1oTjXY9I8z14_qMc-mnZAOoS4o2a_j8HYmlYh0lQj_Qw-1e2R7iO6xqYmeBoquomh9-5gEkba4je2HW08XR7bGhNdGmfiNb01ftMah119QU4q03Y4-eM5eb-_e5s_ZouXh6f5bJEZDjJlem2lGxZaMxWVdcCmqHRhlTRyDWKqAKQt1NoqxhRaUwlnJXOqkk441FiKc3L52zt8-OqxS6tt6KMflCvBeak1K0GKHzDSXr0
ContentType Journal Article
Copyright 2020. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2020. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID ABUWG
AFKRA
AZQEC
BENPR
BHPHI
BKSAR
CCPQU
DWQXO
HCIFZ
PCBAR
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
DOI 10.19637/j.cnki.2305-7068.2020.02.003
DatabaseName ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials Local Electronic Collection Information
ProQuest Central
Natural Science Collection
Earth, Atmospheric & Aquatic Science Collection
ProQuest One Community College
ProQuest Central
SciTech Premium Collection
Earth, Atmospheric & Aquatic Science Database
ProQuest Central Premium
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
DatabaseTitle Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
Earth, Atmospheric & Aquatic Science Database
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Central China
Earth, Atmospheric & Aquatic Science Collection
ProQuest Central
ProQuest One Academic UKI Edition
Natural Science Collection
ProQuest Central Korea
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
DatabaseTitleList Publicly Available Content Database
Database_xml – sequence: 1
  dbid: PIMPY
  name: Publicly Available Content Database
  url: http://search.proquest.com/publiccontent
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
GroupedDBID -01
-0A
5VR
92M
9D9
9DA
ABUWG
AFKRA
AFUIB
ALMA_UNASSIGNED_HOLDINGS
AZQEC
BENPR
BHPHI
BKSAR
CCPQU
DWQXO
GROUPED_DOAJ
HCIFZ
OZF
PCBAR
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQQKQ
PQUKI
PRINS
RT1
T8Q
U1F
U5A
ID FETCH-LOGICAL-a206t-8bc6d000ca43fcd014e785c76a6b0347006c57bc7117ecaf3dc61d7f6d3de8e93
IEDL.DBID PIMPY
ISICitedReferencesCount 8
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000541624800003&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2305-7068
IngestDate Fri Jul 25 09:05:38 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 2
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a206t-8bc6d000ca43fcd014e785c76a6b0347006c57bc7117ecaf3dc61d7f6d3de8e93
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
OpenAccessLink https://www.proquest.com/publiccontent/docview/3229881906?pq-origsite=%requestingapplication%
PQID 3229881906
PQPubID 7241714
ParticipantIDs proquest_journals_3229881906
PublicationCentury 2000
PublicationDate 2020-00-00
PublicationDateYYYYMMDD 2020-01-01
PublicationDate_xml – year: 2020
  text: 2020-00-00
PublicationDecade 2020
PublicationPlace Shijiazhuang
PublicationPlace_xml – name: Shijiazhuang
PublicationTitle Journal of Groundwater Science and Engineering
PublicationYear 2020
SSID ssj0002857228
Score 2.1482875
Snippet Groundwater is important for managing the water supply in agricultural countries like Bangladesh. Therefore, the ability to predict the changes of groundwater...
SourceID proquest
SourceType Aggregation Database
StartPage 118
Title NARX neural network approach for the monthly prediction of groundwater levels in Sylhet Sadar, Bangladesh
URI https://www.proquest.com/docview/3229881906
Volume 8
WOSCitedRecordID wos000541624800003&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
  issn: 2305-7068
  databaseCode: DOA
  dateStart: 20200101
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://www.doaj.org/
  omitProxy: false
  ssIdentifier: ssj0002857228
  providerName: Directory of Open Access Journals
– providerCode: PRVPQU
  databaseName: Earth, Atmospheric & Aquatic Science Database
  issn: 2305-7068
  databaseCode: PCBAR
  dateStart: 20130101
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://search.proquest.com/eaasdb
  omitProxy: false
  ssIdentifier: ssj0002857228
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  issn: 2305-7068
  databaseCode: BENPR
  dateStart: 20130101
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://www.proquest.com/central
  omitProxy: false
  ssIdentifier: ssj0002857228
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: Publicly Available Content Database
  issn: 2305-7068
  databaseCode: PIMPY
  dateStart: 20130101
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: http://search.proquest.com/publiccontent
  omitProxy: false
  ssIdentifier: ssj0002857228
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NS8MwFA-6iXjxAxU_5shBb2ZrkzZJT7LJRA-WsSnM00iTlE1HN9uq7L83abt5EDx5flDKy8v7ynu_HwCXMYuZRYVBhEQ-8mLmosjk_YgLzmSAHUFFSTbBwpCPRkG_Wo_OqrHKlU8sHHWJ9mznto0Tbqu5tB3ztjHDgNtgRm8W78hySNm31opQYxPULfAWr4F6_-Gx_7LuuWDuM1zQrZrE20fMoXwbXBUre5Sw9mtLJm_T1lpmqkfslIie5JerLuLP3d7__vk-2K3yUNgpDecAbOjkEEzDzmAELcSlESXlgDhcoY5Dk95Cky5C84V8MlvCRWofeezBwnkM7XpIor5M6prCmZ1EyuA0gcPlbKJzOBRKpNewKyxliNLZ5Ag83_Webu9RxcWABHZojngkqTIqlMIjsVSmsNKM-5JRQSOHeMxcXumzSDLXZVqKmChJXcViqojSXAfkGNSSeaJPALQI9ZRiE52l8kRAeeDhGJsK39Uqiig-BY2VHsfVhcrGP2o7-1t8DnbssZVdkgao5emHvgBb8jOfZmkT1Lu9sD9oFqV3s7KPb_1cyTw
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V07T8MwED5Bi4CFhwDxxgMwYUicxHYGhvISCKiqAlK34tiOWqhSSAKof4rfiN2kZUBiY2A-KVJyl7vvzufvA9iNWcwsKwz2vCjAfsxcHBncj7ngTIbEEVQUYhOsXuetVtiYgM_RXRi7VjnKicNErfrSzsiPTOCF3JYvWm5QXuvBh-nPsuOrM-PMPUIuzu9PL3EpIYAFcWiOeSSpMn-9FL4XS2X6Ac14IBkVNHI8n5mYkwGLJHNdpqWIPSWpq1hMlac015Zpiey_vGKrUmVPc0vJjkmochoGfgWqjdOTWnM81SE8YGQo6GqgfYCZQ_k07A0vBVKPHT0dyuS5ezi2mf6UOAVnqPejGAwr3MX8f_s2CzBXYmlUK4J_ESZ0sgTdeq3ZQpam05iSYskdjZjTkYHoyEBeZJ6Qd3oD9JLagyobnKgfI3vFJVEfBn6nqGe3qTLUTdDdoNfROboTSqQH6ERY2ROls84yPPzJC69AJeknehWQZdmnlBiEIZUvQspDn8RER8TVKoooWYPNkafaZVLI2t9uWv_dvAMzl_e3N-2bq_r1BszaICmmPptQydM3vQVT8j3vZul2GX8IHv_a018HrRki
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=NARX+neural+network+approach+for+the+monthly+prediction+of+groundwater+levels+in+Sylhet+Sadar%2C+Bangladesh&rft.jtitle=Journal+of+Groundwater+Science+and+Engineering&rft.au=Abdullah+Al+Jami&rft.au=Meher+Uddin+Himel&rft.au=Hasan%2C+Khairul&rft.au=Basak%2C+Shilpy+Rani&rft.date=2020&rft.issn=2305-7068&rft.volume=8&rft.issue=2&rft.spage=118&rft_id=info:doi/10.19637%2Fj.cnki.2305-7068.2020.02.003
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2305-7068&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2305-7068&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2305-7068&client=summon