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Towards Reliability in Smart Water Sensing Technology: Evaluating Classical Machine Learning Models for Outlier

Mimoun Lamrini1,2, Bilal Ben Mahria3, Mohamed Yassin Chkouri2

  • 1Department of Engineering Sciences and Technology (INDI), Vrije Universiteit Brussel (VUB), 1050 Brussels, Belgium.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary

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Smart water sensing relies on accurate data; this study found Support Vector Machines (SVM) effectively detect outliers in electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH sensor data, improving water management analysis.

Area of Science:

  • Environmental Science
  • Data Science
  • Sensor Technology

Background:

  • Smart water sensing is vital for effective water resource management.
  • Sensor data accuracy is challenged by outliers, impacting analysis.
  • Outlier detection is crucial for reliable smart water sensing.

Purpose of the Study:

  • To evaluate machine learning models for outlier detection in smart water sensing data.
  • To assess sensor performance for electrical conductivity (EC), dissolved oxygen (DO), temperature (Temp), and pH.
  • To improve the reliability of water quality monitoring.

Main Methods:

  • Implemented four machine learning models: Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Isolation Forest (iForest).
Keywords:
anomaly detectionmachine learningsensors of water technology

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  • Utilized a dataset from a real-time smart water sensing system in Brussels.
  • Applied outlier detection as a pre-processing step for data visualization.

    • Main Results:

    • Support Vector Machine (SVM) demonstrated superior performance across all tested parameters.
    • SVM achieved high F1-scores: 98.38% for pH, 96.98% for temperature, 97.88% for DO, and 98.11% for EC.
    • Artificial Neural Network (ANN) also yielded significant results, presenting a viable alternative.

    Conclusions:

    • SVM is highly effective for outlier detection in smart water sensing data.
    • Accurate outlier removal enhances the reliability of water quality monitoring.
    • Machine learning models offer robust solutions for improving smart water sensing systems.