Version 1
: Received: 22 August 2023 / Approved: 22 August 2023 / Online: 24 August 2023 (03:20:26 CEST)
How to cite:
Ghoochani, S.; Khorram, M.; Nazemi, N. Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection. Preprints2023, 2023081636. https://doi.org/10.20944/preprints202308.1636.v1
Ghoochani, S.; Khorram, M.; Nazemi, N. Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection. Preprints 2023, 2023081636. https://doi.org/10.20944/preprints202308.1636.v1
Ghoochani, S.; Khorram, M.; Nazemi, N. Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection. Preprints2023, 2023081636. https://doi.org/10.20944/preprints202308.1636.v1
APA Style
Ghoochani, S., Khorram, M., & Nazemi, N. (2023). Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection. Preprints. https://doi.org/10.20944/preprints202308.1636.v1
Chicago/Turabian Style
Ghoochani, S., Mahdis Khorram and Neda Nazemi. 2023 "Uncovering Top-Tier Machine Learning Classifier for Drinking Water Quality Detection" Preprints. https://doi.org/10.20944/preprints202308.1636.v1
Abstract
Water quality assessments are crucial for human health and environmental safeguards. The utilization of a subset of artificial intelligence such as Machine Learning (ML) presents significant impacts to enhance the prediction and classification of water quality. In this research, a set of diverse ML algorithms was evaluated to handle a comprehensive dataset of water quality measurements over an extended period. The aim was to develop a robust approach for accurately forecasting water quality. This approach employed machine learning classifiers such as Logistic Regression (LR), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), K-Nearest Neighbors (KNN), Gaussian Process Classification (GPC), Gaussian Naive Bayes (GNB), Random Forest (RF), Decision Tree (DT), XGBoost, and Multilayer Perceptron (MLP). The water quality parameters assessed for pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes and turbidity. The XGBoost model exhibited the highest accuracy of 89.47% among the classifiers and Stacked Ensemble Classifiers (SEC) improved the prediction further to 92.98%. The findings suggest that XGBoost and the SEC hold promise as reliable approaches for water quality assessments in contrast of artificial intelligence.
Keywords
machine learning; supervised classification; drinking water quality; data-driven; artificial intelligence
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
