AO-SVM: a machine learning model for predicting water quality in the cauvery river

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dc.authoridFemilda Josephin Joseph Shobana Bai /
dc.authorscopusidFemilda Josephin Joseph Shobana Bai / 57810685700
dc.authorwosidFemilda Josephin Joseph Shobana Bai / JKO-0185-2023
dc.contributor.authorVellingiri, J.
dc.contributor.authorKalaivanan, K.
dc.contributor.authorShanmugaiah, Kaliraj
dc.contributor.authorBai, Femilda Josephin Joseph Shobana
dc.date.accessioned2025-04-18T07:51:51Z
dc.date.available2025-04-18T07:51:51Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractWater pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in water sources and determining whether it is safe for human consumption. In this study, we utilized the Cauvery River dataset to develop a model for evaluating water quality. The aim of our research was to proficiently perform feature selection and classification tasks. We introduced a novel technique called the Aquila Optimization Support Vector Machine (AO-SVM), an advanced and effective machine learning system for predicting water quality. Here SVM is used for the classification, and the Aquila algorithm is used for optimizing SVM. The results show that the proposed method achieved a maximum accuracy rate of 96.3%, an execution time of 0.75 s, a precision of 93.9%, a recall rate of 95.1%, and an F1-Score value of 94.7%. The suggested AO-SVM model outperformed all other existing classification models regarding classification accuracy and other parameters.
dc.identifier.citationVellingiri, J., Kalaivanan, K., Shanmugaiah, K., & Bai, F. J. J. S. (2024). AO-SVM: a machine learning model for predicting water quality in the cauvery river. Environmental Research Communications, 6(7), 075025.
dc.identifier.doi10.1088/2515-7620/ad6061
dc.identifier.endpage11
dc.identifier.issn2515-7620
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85199283199
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1088/2515-7620/ad6061
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6498
dc.identifier.volume6
dc.identifier.wosWOS:001270315600001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBai, Femilda Josephin Joseph Shobana
dc.institutionauthoridFemilda Josephin Joseph Shobana Bai / 0000-0003-0249-9506
dc.language.isoen
dc.publisherIOP publishing
dc.relation.ispartofEnvironmental research communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectWater Quality
dc.subjectFeature Selection
dc.subjectAquila Optimization Algorithm
dc.subjectSupport Vector Machine
dc.titleAO-SVM: a machine learning model for predicting water quality in the cauvery river
dc.typeArticle

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