Application of Artificial Neural Networks and MATLAB Models for Assessing Pollution Levels in River Benue, Benue State, Nigeria.

Application of Artificial Neural Networks and MATLAB Models for Assessing Pollution Levels in River Benue, Benue State, Nigeria. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of Artificial Neural Networks and MATLAB Models for Assessing Pollution Levels in River Benue, Benue State, Nigeria. S. K. Egereonu, O. C. Nwokonkwo, E. C. Amadi, B. Okenyi, U. U. Egereonu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6787721/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The impact of pollution on freshwater resources has significant implications for public health and environmental sustainability. This study investigates the pollution levels of River Benue in Benue State, Nigeria, with the objective of evaluating its suitability for potable water supply. A comprehensive analysis was conducted using standard analytical techniques including titrimetry, gravimetry, potentiometry, and spectrophotometry to determine key physico-chemical parameters during both dry and rainy seasons. Results revealed seasonal variations in water quality indicators: total hardness ranged from 22.28 mg/L (dry season) to 41.87 mg/L (rainy season), while electrical conductivity measured 91.35 µS/cm in the rainy season and 54.10 µS/cm in the dry season. Total dissolved solids (TDS) were higher in the rainy season (76.02 mg/L) compared to the dry season (32.31 mg/L), indicating greater ionic presence during rainfall. Dissolved oxygen concentrations were 3.18 mg/L (rainy) and 3.72 mg/L (dry). Phosphate levels were 0.73 mg/L (rainy) and 0.85 mg/L (dry), while sodium concentrations were 2.78 mg/L and 2.95 mg/L, respectively. Calcium values were 1.57 mg/L (rainy) and 2.07 mg/L (dry). Chemical oxygen demand (COD) recorded 114.37 mg/L in the rainy season and 84.97 mg/L in the dry season. Biochemical oxygen demand (BOD) exceeded the World Health Organization (WHO) limits for drinking water, with values of 24.87 mg/L in rainy season and 25.63 mg/L in dry season). Elevated concentrations of heavy metals, including lead 0.00865 mg/L in dry season; 0.00281 mg/L in rainy season and copper 1.24 mg/L in dry season; 0.9734 mg/L in rainy season, poses potential health risks to aquatic life and communities relying on the river for domestic use. A Pearson Product Moment Correlation analysis yielded a coefficient (r) of 0.91, suggesting a strong positive correlation and relatively consistent water quality status across seasons. Furthermore, Artificial Neural Network (ANN) and MATLAB were employed to support the data analysis and predict pollution patterns. The integration of ANN techniques demonstrated high predictive accuracy, highlighting their effectiveness in water quality assessment and environmental monitoring. ANN MATLAB River Benue Pearson Product Moment Pollution level Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6787721","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464477104,"identity":"6cdfdaf0-3a50-49e7-ba53-ecfcf6380ab4","order_by":0,"name":"S. K. Egereonu","email":"","orcid":"","institution":"Federal University of Technology","correspondingAuthor":false,"prefix":"","firstName":"S.","middleName":"K.","lastName":"Egereonu","suffix":""},{"id":464477105,"identity":"bd030611-047b-436a-8168-bee4ca9c2bf7","order_by":1,"name":"O. C. Nwokonkwo","email":"","orcid":"","institution":"Federal University of Technology","correspondingAuthor":false,"prefix":"","firstName":"O.","middleName":"C.","lastName":"Nwokonkwo","suffix":""},{"id":464477106,"identity":"8ba57851-f33c-4bd9-946e-dc0ca721abde","order_by":2,"name":"E. C. Amadi","email":"","orcid":"","institution":"Federal University of Technology","correspondingAuthor":false,"prefix":"","firstName":"E.","middleName":"C.","lastName":"Amadi","suffix":""},{"id":464477107,"identity":"0a6e62c2-4dc2-475c-a5a7-9d596888ca50","order_by":3,"name":"B. Okenyi","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"B.","middleName":"","lastName":"Okenyi","suffix":""},{"id":464477108,"identity":"9f9fe811-4701-49da-9f78-a73c72729ee5","order_by":4,"name":"U. U. 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This study investigates the pollution levels of River Benue in Benue State, Nigeria, with the objective of evaluating its suitability for potable water supply. A comprehensive analysis was conducted using standard analytical techniques including titrimetry, gravimetry, potentiometry, and spectrophotometry to determine key physico-chemical parameters during both dry and rainy seasons.\u003c/p\u003e \u003cp\u003eResults revealed seasonal variations in water quality indicators: total hardness ranged from 22.28 mg/L (dry season) to 41.87 mg/L (rainy season), while electrical conductivity measured 91.35 \u0026micro;S/cm in the rainy season and 54.10 \u0026micro;S/cm in the dry season. Total dissolved solids (TDS) were higher in the rainy season (76.02 mg/L) compared to the dry season (32.31 mg/L), indicating greater ionic presence during rainfall. Dissolved oxygen concentrations were 3.18 mg/L (rainy) and 3.72 mg/L (dry). Phosphate levels were 0.73 mg/L (rainy) and 0.85 mg/L (dry), while sodium concentrations were 2.78 mg/L and 2.95 mg/L, respectively. Calcium values were 1.57 mg/L (rainy) and 2.07 mg/L (dry). Chemical oxygen demand (COD) recorded 114.37 mg/L in the rainy season and 84.97 mg/L in the dry season. Biochemical oxygen demand (BOD) exceeded the World Health Organization (WHO) limits for drinking water, with values of 24.87 mg/L in rainy season and 25.63 mg/L in dry season). Elevated concentrations of heavy metals, including lead 0.00865 mg/L in dry season; 0.00281 mg/L in rainy season and copper 1.24 mg/L in dry season; 0.9734 mg/L in rainy season, poses potential health risks to aquatic life and communities relying on the river for domestic use.\u003c/p\u003e \u003cp\u003eA Pearson Product Moment Correlation analysis yielded a coefficient (r) of 0.91, suggesting a strong positive correlation and relatively consistent water quality status across seasons. Furthermore, Artificial Neural Network (ANN) and MATLAB were employed to support the data analysis and predict pollution patterns. The integration of ANN techniques demonstrated high predictive accuracy, highlighting their effectiveness in water quality assessment and environmental monitoring.\u003c/p\u003e","manuscriptTitle":"Application of Artificial Neural Networks and MATLAB Models for Assessing Pollution Levels in River Benue, Benue State, Nigeria.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-11 03:21:36","doi":"10.21203/rs.3.rs-6787721/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a086fe7e-cae3-45df-825b-bc092054c381","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T10:39:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-11 03:21:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6787721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6787721","identity":"rs-6787721","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}