Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Reliable Method of Predicting Water Quality Using Supervised Machine Learning Model Subject The topic of the resource Gradient boosting; Indian rivers; Monitoring expert system; Regression Analysis Supervised machine learning; Water quality index Description An account of the resource Water contributes to around 70% of the world's exterior and is perhaps the primary source essential to supporting life. The rapid growth of urban and industrial geographies has prompted a disintegration of the quality of water at a concerning pace, bringing about nerve-racking sicknesses. Water quality has been expectedly assessed through costly and tedious lab and measurable examinations, which render the contemporary thought of continuous observing disputable. The disturbing results of helpless water quality require an elective strategy, which is speedier and more economical. With this inspiration, this exploration investigates a progression of administered AI calculations to appraise the Water Quality Index (WQI), which acts as a unique attribute to express the generic nature of water. The proposed system utilizes multiple info boundaries, specifically, temperature, pH, dissolved O2 concentration, and all-out broken down molecules. Of the multitude of utilized regression calculations and slope boosting, the water quality index can be expected most productively, with an MSE of 0.27. The propositioned study accomplishes acceptable precision by utilizing a minimum number of features to improve the chances of it getting implemented progressively in water quality recognition frameworks. 2022 IEEE. Creator An entity primarily responsible for making the resource Nanjundan P.; George J.P.; Vij A. Source A related resource from which the described resource is derived IEEE International Conference on Data Science and Information System, ICDSIS 2022 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSIS55133.2022.9915819" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSIS55133.2022.9915819</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141476174&amp;doi=10.1109%2FICDSIS55133.2022.9915819&amp;partnerID=40&amp;md5=1359fdaf582b3a9faa7341755b93563f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141476174&amp;doi=10.1109%2fICDSIS55133.2022.9915819&amp;partnerID=40&amp;md5=1359fdaf582b3a9faa7341755b93563f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166549801-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nanjundan P., Christ University, India; George J.P., Christ University, India; Vij A., Bajaj Allianz Life, Department of Analytics, India