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An efficient IoT based smart water quality monitoring system

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Abstract

Globally, water resources play a vital role regards to environment and health. Accurate forecasting of water quality is the key to enhancing water management. To identify the water quality effects and provide an automated water quality monitoring and testing system that can support in guaranteeing the safety of the water around the world. Therefore, This paper presents an IoT-based water quality system along with an efficient prediction method based on machine learning techniques to forecast at scale the water quality for competent decision support making in IoT-based smart water quality and monitoring systems in the context of smart cities. This water quality monitoring and testing system use the Internet of Things and forecasting-based machine learning algorithms. Forecasting is an indispensable task in the data prediction journey which can help the water provider entities to plan better, set goals, and detect abnormal events. Therefore, this work describes an experimental work to forecast at scale the water quality and proposes the measurement of the Water Quality Index (WQI) for drinking water and labels the dataset with WQI values. Likewise, it provides a comparative study between LSTM and Facebook prophet in the era of water quality which can help the data providers and business analysts to make better decisions based on forecasting water quality data. The result shows that the Facebook prophet performs better in terms of accuracy, performance, and resource utilization.

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Acknowledgments

This work is a part of the project “Smart Spout: A Water Quality System based on Big Data Analytics and Internet of Things in the Context of Smart City Initiatives in Egypt” funded by ITAC under grant ID “CFP178”. Also, this work contains data supplied by Natural Environment Research.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menoufia, Egypt

    Ezz El-Din Hemdan, Marwa Shouman, Ayman El-Sayed & Abdullah N. Moustafa

  2. Principle Data and Knowledge, Bayer, Berlin, Germany

    Youssef M. Essa

Authors
  1. Ezz El-Din Hemdan
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  2. Youssef M. Essa
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  3. Marwa Shouman
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  4. Ayman El-Sayed
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  5. Abdullah N. Moustafa
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Correspondence to Ezz El-Din Hemdan.

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Hemdan, E.ED., Essa, Y.M., Shouman, M. et al. An efficient IoT based smart water quality monitoring system. Multimed Tools Appl 82, 28827–28851 (2023). https://doi.org/10.1007/s11042-023-14504-z

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  • DOI: https://doi.org/10.1007/s11042-023-14504-z

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