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Integrated GIS and multivariate statistical approach for spatial and temporal variability analysis for lake water quality index

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dc.citedby5
dc.contributor.authorSubramaniam P.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorFai C.M.en_US
dc.contributor.authorAbdul Malek M.en_US
dc.contributor.authorKumar P.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorSherif M.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid57223344990en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57214146115en_US
dc.contributor.authorid57221404206en_US
dc.contributor.authorid57206939156en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid7005414714en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2024-10-14T03:21:44Z
dc.date.available2024-10-14T03:21:44Z
dc.date.issued2023
dc.description.abstractIt is critical to monitor water quality to keep water bodies ecologically healthy and facilitate the sustainable development of Kenyir Lake. Water quality differs temporally and spatially and is affected by several factors. Typically, water quality inspection systems are cost- and labour-intensive depending on water quality indicator count and sampling frequency. Optimising the frequency and location of water quality sampling is crucial. This study focused on collecting water samples from 22 locations in Kenyir Lake during different seasons (normal, dry, and wet). The study aimed to assess the spatial and temporal variations in the water quality of Kenyir Lake based on multivariate statistical methods. In this study, the following water quality parameters were selected for analysis: temperature, dissolved oxygen (DO), pH, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), and ammoniacal nitrogen (NH3-N). In addition, a water quality index was also calculated. GIS software was used to assess water quality data, and various multivariate statistical methods like cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were employed. The outcome shows minor spatial differences concerning Kenyir Lakeen_US
dc.description.abstracthowever, the temporal variations were noteworthy during this study duration. Cluster analysis divided the locations into 3 clusters with TSS being key parameter affecting the spatial differences in water quality. Stepwise discriminant analysis based on three parameters, pH, temperature, and TSS, produced the associated classification matrix that correctly estimated 69.7% of the input. NH3-N and TSS were found to be the two critical aspects that affect water quality during dry, wet, or normal climatic conditions. � 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2190490
dc.identifier.doi10.1080/23311916.2023.2190490
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85150692153
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85150692153&doi=10.1080%2f23311916.2023.2190490&partnerID=40&md5=5c220a92f8ecddd5f54a4b0c99264339
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34685
dc.identifier.volume10
dc.publisherCogent OAen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleCogent Engineering
dc.subjectcluster analysis
dc.subjectdiscriminant analysis
dc.subjectKenyir Lake
dc.subjectprincipal component analysis
dc.subjectwater quality
dc.titleIntegrated GIS and multivariate statistical approach for spatial and temporal variability analysis for lake water quality indexen_US
dc.typeArticleen_US
dspace.entity.typePublication
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