Classification of Water Potability Using Machine Learning Algorithms

Water is the most important resource for sustaining life. Everyone has the right to have access to pollution-free water. The achievement of safe drinking water leads to tangible benefits to health. According to the World Health organization (WHO), the number of global deaths due to water diseases is...

Full description

Saved in:
Bibliographic Details
Published in2022 International Conference on Data Analytics for Business and Industry (ICDABI) pp. 454 - 458
Main Authors Yusuf, Husain, Alhaddad, Salman, Yusuf, Salman, Hewahi, Nabil
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.10.2022
Subjects
ANN
Artificial neural networks
Drinking
Industries
KNN
Learning
Machine
Machine learning algorithms
Organizations
Potability
Quality
Safe
Support vector machines
SVM
Water
Water pollution
Water quality
Online AccessGet full text

Cover

More Information
Summary:Water is the most important resource for sustaining life. Everyone has the right to have access to pollution-free water. The achievement of safe drinking water leads to tangible benefits to health. According to the World Health organization (WHO), the number of global deaths due to water diseases is about two and a half million deaths per year. Therefore, it is important to study new applications for analyzing and classifying safe water quality. In this research, the classification algorithms of K Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM) are used to classify the potability of the drinking water. RF and DT achieved the highest performance with an accuracy of 83.78% and 74.98% respectively. The lowest classifier accuracy is LR with 48.74%. In addition, the most important features of the RF model are pH, followed by Hardness, followed by Sulfate. On other hand, the least important feature is Turbidity.
DOI:10.1109/ICDABI56818.2022.10041667