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Eamlm: Enhanced Automated Machine Learning Model for IoT Based Water Quality Analysis with Real-Time Dataset

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Abstract—

In the present decade, the quality of water has become the major concern, because of the rapid increase of pollution on water resources. This has been a great threat for all living beings in the planet. Hence, there is always a demand on an efficient model for water quality analysis. With that note, this paper develops an enhanced automated machine learning model called EAMLM. Moreover, the proposed model utilized Internet of Things (IoT) based sensors to obtain the quality factors of water such as pH rate, temperature, nitrogen, phosphorous, total hardness and total dissolved solids (TDS). The model integrates the IoT analysis with the operations of machine learning methods to evaluate the real-time data of water samples obtained from local areas. In particular, the EAMLM algorithm is framed with the combined efficiencies of modified ranking based K-nearest neighbors and random forest (RF) model. Further, Raspberry Pi3 is low cost kit embedded for sample testing and the model is simulated and evaluated using WEKA tool. The classification results show that the EAMLM provided better accuracy than other traditional models.

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Authors and Affiliations

  1. Department of Data Science, The American College, Madurai, India

    D. Senthil Kumar

  2. Department of Computer Science and Engineering, Saveetha School of Engineering, SIMTAS, Chennai, India

    S. S. Arumugam

  3. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed University), Chennai, Tamilnadu, India

    Lordwin Cecil Prabhaker M.

  4. Department of Electronics and Communication Engineering, Anna University Regional Campus Madurai, Madurai, India

    Daisy Merina R.

Authors
  1. D. Senthil Kumar
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  2. S. S. Arumugam
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  3. Lordwin Cecil Prabhaker M.
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  4. Daisy Merina R.
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Correspondence to Lordwin Cecil Prabhaker M..

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Senthil Kumar, D., Arumugam, S.S., Lordwin Cecil Prabhaker M. et al. Eamlm: Enhanced Automated Machine Learning Model for IoT Based Water Quality Analysis with Real-Time Dataset. Aut. Control Comp. Sci. 58, 66–77 (2024). https://doi.org/10.3103/S0146411624010085

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