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IoT Regulated Water Quality Prediction Through Machine Learning for Smart Environments

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Machine Learning for Smart Environments/Cities

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 121))

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Abstract

Smart cities have been contingent on the concept of Internet of Things since the beginning, the only measurable gap is the means to achieve it. Smart cities are not limited to urban housing but are more sectarian in the suburbs and require a means to devise efficient mechanisms and systems that can support sustainable growth. Industrial suburbs are the major pollutants that are the primary focus of this research. This paper works on the data of recycled wastewater procured from industrial use, which otherwise is directly discharged into the rivers. We collect the data using IoT sensors, these sensors are responsible for obtaining information about the data, also for scrutinizing and maintaining the water quality. The data contains major features that signify and influence the water quality, these parameters are used for the calculation of the water quality index. Finally, we train and predict this quality index using 3 machine learning algorithms-Decision Tree, Random Forest, and Deep Neural Networks. The evaluation metrics consist of RMSE, MAD, MAPE, and R squared value to identify and determine the model with the best performance.

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Author information

Authors and Affiliations

  1. University of Florida, Gainesville, USA

    Ekleen Kaur

Authors
  1. Ekleen Kaur
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Editor information

Editors and Affiliations

  1. Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, Oliveira do Hospital, Portugal

    Gonçalo Marques

  2. Grupo de Investigación BISITE, Departamento de Informática y Automática, Facultad de Ciencias, Instituto de Investigación Biomédica de Salamanca, Universidad de Salamanca, Salamanca, Spain

    Alfonso González-Briones

  3. Universidad Carlos III DE Madrid, Colmenarejo, Spain

    José Manuel Molina López

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Kaur, E. (2022). IoT Regulated Water Quality Prediction Through Machine Learning for Smart Environments. In: Marques, G., González-Briones, A., Molina López, J.M. (eds) Machine Learning for Smart Environments/Cities. Intelligent Systems Reference Library, vol 121. Springer, Cham. https://doi.org/10.1007/978-3-030-97516-6_3

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