{"refrec":{"BRefID":353545,"RR":"<b>Jamei, M.; Karbasi, M.; Malik, A.; Abualigah, L.; Islam, A.R.M.T.; Yaseen, Z.M.</b> (2022). Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh. <i>NPG Scientific Reports 12(1)</i>: 11165. <a href=\"https://dx.doi.org/10.1038/s41598-022-15104-x\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-022-15104-x</a>","BEntID":351255,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>NPG Scientific Reports 12(1)</i>: 11165. <a href=\"https://dx.doi.org/10.1038/s41598-022-15104-x\" target=\"_blank\">https://dx.doi.org/10.1038/s41598-022-15104-x</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Jamei, M.; Karbasi, M.; Malik, A.; Abualigah, L.; Islam, A.R.M.T.; Yaseen, Z.M.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Jamei, M. <i>et al.</i>","Englishabstract":"The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253&nbsp;ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534&nbsp;ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388&nbsp;ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl<sup>−</sup> (mg/l), Mg<sup>2+</sup> (mg/l), Na<sup>+</sup> (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-06-15 01:33:43.055007","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2022,"MonPub":null,"DateUpdate":"2022-07-07","DateCreate":"2022-07-07","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000819858600085","VABBcode":null,"OpenAcc":1,"DOI":"10.1038/s41598-022-15104-x"},"refs":null,"anarec":{"AnaID":353545,"PubliDate":2022,"Pagination":"11165","XtraPublOfAnaID":null,"ISBN":null,"Volume":"12","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":208093,"SerRR":"Scientific Reports (Nature Publishing Group). 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ISSN 2045-2322; e-ISSN 2045-2322","StandardTitleSer":"Scientific Reports (Nature Publishing Group)","ISSN":"2045-2322","AbbrevSer":"NPG Scientific Reports","StandardTitleMon":null,"StartPage":11165,"Pages":null,"ToPubliDate":null,"BRefBibLvlCode":"S","SerNotes":null},"monrec":null,"serrec":null,"relations":null,"relationsRev":null,"addrec":null,"othpubs":null,"ownerships":null,"authors":[{"AutName":"Jamei","Firstname":"Mehdi","Initials":"M.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":351255,"AutID":495067,"OrderNr":1,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"Karbasi","Firstname":"Masoud","Initials":"M.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":351255,"AutID":495068,"OrderNr":2,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"Malik","Firstname":"Anurag","Initials":"A.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":351255,"AutID":495069,"OrderNr":3,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"Abualigah","Firstname":"Laith","Initials":"L.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":351255,"AutID":495070,"OrderNr":4,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"Islam","Firstname":"Abu","Initials":"A.R.M.T.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":351255,"AutID":495071,"OrderNr":5,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null},{"AutName":"Yaseen","Firstname":"Zaher","Initials":"Z.M.","Affiliation":null,"Discriminator":null,"CorporateFlag":0,"BEntID":351255,"AutID":495072,"OrderNr":6,"DegrID":null,"EditorFlag":0,"CorrespFlag":0,"IllustratorFlag":0,"ReviserFlag":0,"TranslatorFlag":0,"InsAcronym":null,"InsFSN":null,"ORCID":null,"PersID":null,"InsID":null}],"mapdetails":null,"datasets":null,"monographs":null,"monparts":null,"serparts":null,"BEntOpen":null,"BEntPrivate":null,"availability":[{"BInstID":378878,"LibID":36,"BRefID":353545,"EmbargoDate":null,"FullEmbargoDate":null,"PhysMedID":16,"hasOCRd":1,"ShelfLocCode":"378878","RFID":null,"PaidValue":null,"Medium":"Server","Description":"VLIZ Open Access","Acronym":"VLIZ","Library":"Vlaams Instituut voor de Zee","DutchTerm":"Open access","URL":null,"ClassifID":53,"Classification":"Open access","ReqLink":null,"ClassifTypID":1,"URLLocation":"https://www.vliz.be/imisdocs/publications/","SubDir":null,"InternalReq":0,"LoggedInReq":0,"Disclaimer":null,"DutchDisclaimer":null,"FileFormat":".pdf","FileDescr":"pdf","InsPub":1,"InsID":36,"FileFormID":6,"LendableFlag":1,"PublicFlag":1,"orderLib":"A","Notes":null,"AccConID":null,"AccessConstraint":null,"LicURL":null}],"litstyles":null,"thespers":null,"arch2discl":null,"SERpubls":[{"PublName":"Nature Publishing Group","City":"London"}],"MONpubls":null,"pictures":[],"thestermsPath":null,"thestermsASFA":null,"taxtermsASFA":null,"geotermsASFA":null,"collections":null,"conf":null,"proj":null,"Physdatasets":null,"spcols":null,"doi":null,"publs":null,"serparttypes":null,"monauthors":null,"MParts":null,"SParts":null,"hLibs":null,"langs":[{"BEntID":351255,"AbstractFlag":0,"LangID":15,"LangCode":"en","Lang":"English","DutchTerm":"Engels","LangCodeExtended":"eng"},{"BEntID":351255,"AbstractFlag":1,"LangID":15,"LangCode":"en","Lang":"English","DutchTerm":"Engels","LangCodeExtended":"eng"}],"urls":[{"URL":"https://dx.doi.org/10.1038/s41598-022-15104-x","externalID":"10.1038/s41598-022-15104-x","URLTypeCode":"DOI","URLID":104241,"URLTypID":13,"URLType":"DOI","URLPrefix":"http://dx.doi.org/"}],"thesterms":null,"taxterms":null,"geoterms":null,"othterms":null,"asfacodes":null,"asfa2codes":null,"thestermsFRIS":null,"taxtermsFRIS":null,"geotermsFRIS":null,"othtermsFRIS":null,"resmessage":"","complete":1,"sessions":{"newSesName":"Bouchti, Zohra, Z.","newSesDate":{"date":"2022-07-07 08:33:12.493000","timezone_type":3,"timezone":"Europe/Brussels"},"updSesName":"Bouchti, Zohra, Z.","updSesDate":{"date":"2022-07-07 08:33:12.493000","timezone_type":3,"timezone":"Europe/Brussels"}}}