Design of a real–time water quality monitoring and control system using Internet of Things (IoT)

Chafa, Allen T.; Chirinda, Gibson P.; Matope, Stephen

doi:10.1080/23311916.2022.2143054

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…Analytical instruments such as atomic and molecular spectrometers, electrochemical analysis instruments, nuclear magnetic resonance, X-rays, and mass spectrometers are available [126]. While traditional methods for monitoring water quality, such as field measurements and laboratory analysis, are widely accepted for their accuracy and reliability, they are often constrained by various factors, including limited spatiotemporal resolution, high costs, and time-consuming and labor-intensive processes [127][128][129][130]. These limitations make it challenging to obtain real-time data and monitor water quality on a large scale.…”

Section: Satellite Applications For Water Resources and Quality Monit...mentioning

confidence: 99%

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author’s presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution.

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Section: Satellite Applications For Water Resources and Quality Monit...mentioning

confidence: 99%

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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Real-Time Monitoring and Fault Detection in AI-Enhanced Wastewater Treatment Systems

Mohanty,

Mohapatra

2024

Springer Water

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Real-Time Measurement Analysis of Rainwater Quality Based on the Internet of Things

Ramadhan,

Alawi,

Sabilla

et al. 2023

2023 29th International Conference on Telecommunications (ICT)

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Design of a real–time water quality monitoring and control system using Internet of Things (IoT)

Cited by 6 publications

References 17 publications

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Real-Time Monitoring and Fault Detection in AI-Enhanced Wastewater Treatment Systems

Real-Time Measurement Analysis of Rainwater Quality Based on the Internet of Things

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