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Water potability classification based on hybrid stacked model and feature selection.

Ahmed M Elshewey1, Rasha Y Youssef2, Hazem M El-Bakry3

  • 1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O. Box: 43221, Suez, Egypt. ahmed.elshewey@fci.suezuni.edu.eg.

Environmental Science and Pollution Research International
|March 6, 2025
PubMed
Summary
This summary is machine-generated.

Accurate water potability categorization is crucial for clean water. Ensemble learning, particularly stacking models, significantly improves water quality prediction accuracy and reliability.

Keywords:
Feature selectionMachine learningStacking ensembleWater potabilityWater potability classification

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Area of Science:

  • Environmental Science
  • Data Science
  • Machine Learning

Background:

  • Accurate water quality categorization is essential for ensuring clean water access.
  • Existing methods for water potability (WP) assessment require robust predictive models.
  • A public Kaggle dataset comprising 3276 water bodies with various quality metrics was utilized.

Purpose of the Study:

  • To prepare a water potability dataset for machine learning.
  • To identify the most important features for water quality classification using optimization algorithms.
  • To evaluate and compare the performance of various machine learning classifiers for water potability prediction.
  • To enhance predictive performance through ensemble learning techniques, specifically stacking.

Main Methods:

  • Data preprocessing involved median imputation, normalization, and Synthetic Minority Over-sampling Technique (SMOTE) for class imbalance.
  • Feature selection (FS) was performed using Binary Particle Swarm Optimization (BPSO) and Binary Whale Optimization Algorithm (BWAO) to identify key water quality indicators.
  • Multiple classifiers including Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), Extra Tree (ET), Decision Tree (DT), and XGBoost were trained and evaluated.
  • A stacking ensemble model was developed using Logistic Regression as a meta-learner with RF, ET, and XGBoost as base learners.

Main Results:

  • BPSO identified a subset of seven essential features with an average error of 0.3745.
  • The Extra Tree (ET) classifier achieved the highest performance among individual models, with 70.63% accuracy and 71.17% F1-score.
  • The stacking model demonstrated improved performance, achieving 69.53% accuracy, 71.17% F1-score, and 77.62% AUC.
  • Ensemble learning, particularly stacking, proved effective in creating a robust water quality categorization framework.

Conclusions:

  • Ensemble learning methods, especially stacking, offer a significant improvement in water potability categorization accuracy.
  • Feature selection using BPSO effectively identified crucial water quality parameters.
  • Stacking models provide a feasible and powerful approach for enhanced water quality measurement and management.