Human-Centric Intelligent Systems

Volume 1, Issue 3-4, December 2021, Pages 86 - 97

Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

Authors
Md. Mehedi Hassan1, *, Md. Mahedi Hassan2, Laboni Akter3, Md. Mushfiqur Rahman4, Sadika Zaman1, Khan Md. Hasib5, Nusrat Jahan6, Raisun Nasa Smrity2, Jerin Farhana7, M. Raihan1, Swarnali Mollick8
1Computer Science and Engineering, North Western University, Khulna, Bangladesh
2Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
3Biomedical Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
4Department of Statistics, University of Dhaka, Dhaka, Bangladesh
5Computer Science and Engineering, Ahsanullah University of Science & Technology, Dhaka, Bangladesh
6Department of Pharmacy, Khulna University, Khulna, Bangladesh
7Department of Pharmacy, University of Development Alternative, Dhaka, Bangladesh
8Computer Science and Engineering, Northern University of Business & Technology, Khulna, Bangladesh
*Corresponding author. Email: mehedihassan@ieee.org
Corresponding Author
Md. Mehedi Hassan
Received 5 October 2021, Accepted 22 November 2021, Available Online 10 December 2021.
DOI
https://doi.org/10.2991/hcis.k.211203.001How to use a DOI?
Keywords
River water; water quality prediction; WQI; NN
Abstract

The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model’s feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality, with Variable Importance values of 74.78, 36.805, 81.494, 105.770, 105.166, and 130.173, respectively.

Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Human-Centric Intelligent Systems
Volume-Issue
1 - 3-4
Pages
86 - 97
Publication Date
2021/12/10
ISSN (Online)
2667-1336
DOI
https://doi.org/10.2991/hcis.k.211203.001How to use a DOI?
Copyright
© 2021 The Authors. Publishing services by Atlantis Press International B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Md. Mehedi Hassan
AU  - Md. Mahedi Hassan
AU  - Laboni Akter
AU  - Md. Mushfiqur Rahman
AU  - Sadika Zaman
AU  - Khan Md. Hasib
AU  - Nusrat Jahan
AU  - Raisun Nasa Smrity
AU  - Jerin Farhana
AU  - M. Raihan
AU  - Swarnali Mollick
PY  - 2021
DA  - 2021/12/10
TI  - Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms
JO  - Human-Centric Intelligent Systems
SP  - 86
EP  - 97
VL  - 1
IS  - 3-4
SN  - 2667-1336
UR  - https://doi.org/10.2991/hcis.k.211203.001
DO  - https://doi.org/10.2991/hcis.k.211203.001
ID  - Hassan2021
ER  -