UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 10 Issue 4
April-2023
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIR2304A27


Registration ID:
512796

Page Number

k189-k198

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Title

Machine Learning Algorithms Comparative Study

Abstract

Water quality index (WQI) is widely used to assess and classify groundwater and surface water quality and plays an important role in water resource management. In recent decades, artificial intelligence (AI) in the form of machine learning models has been applied to solve various environmental engineering problems, such as water quality modelling. Machine learning models represent a major innovation in research for monitoring and controlling a variety of engineering processes, using their algorithms. The proposed models have been evaluated and tested using certain statistical parameters. The results have revealed that the Random Forest algorithm has achieved the highest accuracy level compared to other machine learning models like decision tree, MLP and gradient boosting.

Key Words

Artificial Intelligence, Machine Learning, Water Quality Index, Model comparison

Cite This Article

"Machine Learning Algorithms Comparative Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.k189-k198, April-2023, Available :http://www.jetir.org/papers/JETIR2304A27.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Machine Learning Algorithms Comparative Study", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppk189-k198, April-2023, Available at : http://www.jetir.org/papers/JETIR2304A27.pdf

Publication Details

Published Paper ID: JETIR2304A27
Registration ID: 512796
Published In: Volume 10 | Issue 4 | Year April-2023
DOI (Digital Object Identifier):
Page No: k189-k198
Country: Bengaluru, Karnataka, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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