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Water Quality Assessment Using the Random Forest Classification Model
Faiza Bouchraki1, Samir Hamchaoui1, Louiza Lysa Ayad2
1Université de Bejaia, Faculté de Technologie, Département d'Hydraulique, Laboratoire de Recherche en Hydraulique Appliquée et Environnement (LRHAE), Bejaia, Algeria.
Summary
An automated Random Forest model accurately classifies water quality using mixed real and synthetic data. This system aids drinking water managers in rapid decision-making for improved water safety and compliance.
Area of Science:
- Environmental Science
- Computer Science
- Data Science
Background:
- Effective water quality monitoring is crucial for public health.
- Current methods can be time-consuming and require manual data interpretation.
- Automated systems can enhance efficiency and accuracy in water quality assessment.
Purpose of the Study:
- To develop and validate an automated classification model for water quality assessment.
- To create a user-friendly web platform for real-time water quality monitoring and decision support.
- To improve the speed and reliability of identifying non-compliant water samples.
Main Methods:
- Utilized a Random Forest classification model.
- Employed a mixed dataset of real-world and synthetic data for balanced model training.
- Implemented stratified cross-validation and tested on a real-world data set.
- Developed a web platform for automated data entry, classification, and result visualization.
Main Results:
- Achieved an average macro F1 score of 0.98 across all classes during cross-validation.
- Demonstrated high predictive accuracy (99%) for majority classes on real-world test data.
- The developed web platform automates data processing and provides instant results.
Conclusions:
- Automated computational approaches significantly enhance water quality management.
- The Random Forest model and web platform offer a valuable tool for drinking water service managers.
- Further research is needed on data balancing and real-world validation for robust water quality monitoring systems.
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