Water Quality Evaluation Using Machine Learning Techniques

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kajal rajendra gavali; A.S.Gundale

Water Quality Evaluation Using Machine Learning Techniques

Authors: kajal rajendra gavali, A.S.Gundale

Unique Paper ID: 161008
Volume: 10
Issue: 2
PageNo: 1017-1024

Keywords: Machine learning prediction monitoring data mining classification water quality index water quality param- eters .

Abstract:
One of the most significant and serious issues currently affecting mankind is the degradation of natural water resources, such as rivers and lakes. Polluted water has longterm repercussions on all facets of existence. In order to maximise your water quality, it is crucial to manage your water resources. The impacts of water contents can be efficiently managed if data are analysed and water quality can be forecasted.This studyâ€™s objective is to develop a model for predicting quality of water is based on measurements of water quality using machine learning. With some data obtained through machine learning, models made of algorithms can be created. The collected data will be preprocessed, divided into training and testing portions, and exposed to machine learning classification techniques for a better assessment of parametric findings. Some of the classification type techniques used in this work are Decision Tree, LinearSVC, Random Forest, GradientBoosting, SGD, and KNeighbour. Each modelâ€™s performance indicators are computed and are different from one another. Hyper tuning is a method for raising perfor- mance metrics for models of machine learning

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Cite This Article

ISSN: 2349-6002
Volume: 10
Issue: 2
PageNo: 1017-1024

Water Quality Evaluation Using Machine Learning Techniques

Available:https://ijirt.org/article?manuscript=161008

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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