Water quality monitoring: from conventional to emerging technologies

Ahmed, Umair; Mumtaz, Rafia; Anwar, Hirra; Mumtaz, Sadaf; Qamar, Ali Mustafa

doi:10.2166/ws.2019.144

Cited by 85 publications

(70 citation statements)

References 13 publications

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“…However, it could not be true for other sources such as water acquired from ponds, lakes, rivers, or rains. Due to this reason, it is necessary to monitor water quality continuously so that the spread of any damage to consumers could be prevented timely [19,32,47,48]. In general, water quality is defined as: "The water suitability for a particular application (e.g., drinking), which is based on its physical, chemical and biological characteristics" [20,31,32].…”

Section: Water Qualitymentioning

confidence: 99%

“…Many stakeholders such as WHO/USEPA are continuously working on developing guidelines and standards related to water quality. Though the list of water quality monitoring (WQM) parameters is very large [16], a limited set of significant WQM parameters is generally utilized to monitor water quality [19,31,35,49,50]. On the basis of such parameters, a water quality index generates a single number expressing the overall quality of water acquired from a target locality at a specific time.…”

Section: Water Quality Index (Wqi)mentioning

confidence: 99%

“…On the top of pollution and studies pointing out to global-warming's impact on water resources [19][20][21], the World Water Council (WWC) is predicting a global population increase by 40% to 50% over the next 50 years [22]. This significant growth, in conjunction…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications

Jan

Min‐Allah

Düştegör

2021

Water

143

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Safe water is becoming a scarce resource, due to the combined effects of increased population, pollution, and climate changes. Water quality monitoring is thus paramount, especially for domestic water. Traditionally used laboratory-based testing approaches are manual, costly, time consuming, and lack real-time feedback. Recently developed systems utilizing wireless sensor network (WSN) technology have reported weaknesses in energy management, data security, and communication coverage. Due to the recent advances in Internet-of-Things (IoT) that can be applied in the development of more efficient, secure, and cheaper systems with real-time capabilities, we present here a survey aimed at summarizing the current state of the art regarding IoT based smart water quality monitoring systems (IoT-WQMS) especially dedicated for domestic applications. In brief, this study probes into common water-quality monitoring (WQM) parameters, their safe-limits for drinking water, related smart sensors, critical review, and ratification of contemporary IoT-WQMS via a proposed empirical metric, analysis, and discussion and, finally, design recommendations for an efficient system. No doubt, this study will benefit the developing field of smart homes, offices, and cities.

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Section: Water Qualitymentioning

confidence: 99%

Section: Water Quality Index (Wqi)mentioning

confidence: 99%

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IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications

Jan

Min‐Allah

Düştegör

2021

Water

143

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“…They verified the proposed idea utilizing three months' data of water quality such as pH obtained from a real-time water quality monitoring station of a river system. Ahmed et al (2020) used IoT systems for water quality monitoring in real-time and machine learning methods like ANN and SVM to predict WQI, and detect abnormal events, namely, purposeful pollution to allow real-time pollution detection and control operation. Their proposed method also applied regression, correlation analysis, hierarchical clustering, etc.…”

Section: Related Workmentioning

confidence: 99%

Deep learning-based production assists water quality warning system for reverse osmosis plants

Udayakumar¹,

Subiramaniyam²

2020

H2Open Journal

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Classifying water quality irregularities in reverse osmosis (RO) production plants requires suitable systems to provide intelligent warnings to the operators or supervisors who are engaged in executing corrective procedures applicable to production. The suggested deep learning methods are of utmost importance to identify at once variations in water quality irregularities in plants through competent classification methods, thereby enabling a reduction of burden for operators. In this paper, two types of LSTM-CNN based classification techniques are suggested to classify water quality features temporally into grades based on corrective actions that classify irregularity conditions of water quality on the basis of corrections. Distinct control methods are used for experiments to find water quality irregularities from variables, namely, pH, TDS, ORP, and EC, which aim to assist the production line. This proposed method enables automatic diagnosis and warning systems about water quality in RO plants. For classification, LSTM-CNN was trained with data recorded from eight plants of west and north parts of Chennai region. This research is meant to demonstrate particularly the top-level classification job for quality alerts. The features obtained from 4,096 time series array data using LSTM-CNNs achieved sensitivity to 97% and accuracy to 98%.

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“…(8) ML algorithms help to estimate water quality parameters in less time and to provide a real-time measurement. (14) ML algorithms in cooperation with RS imagery reduce the human effort of analyzing big data and are highly cost-effective while producing very accurate results. (15)(16)(17)(18) Considering the literature gap, we present a comprehensive review of the application of ML algorithms for water quality parameter estimation using RS imagery.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

Wagle¹,

Acharya²,

Lee³

2020

Sensors and Materials

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Water is an integral aspect of the world necessary for living creatures to thrive. Owing to unplanned urbanization, rapid industrialization, and uncontrollable human intervention, water quality is gradually degrading. This affects not only marine animals but also humans. Thus, the quality of water should be examined regularly. Water quality parameters should be estimated to monitor water quality. In general, water quality parameters are measured by in situ measurements. Although these measurements are accurate, they are costly and do not provide real-time spatial and temporal changes in water quality. To overcome this limitation, water quality parameters can be estimated using machine learning (ML) along with remote sensing (RS) data. A combination of ML and RS data is a powerful approach for the routine assessment of spatial and temporal variations in water quality parameters. In this paper, some articles based on this approach are reviewed. By analyzing the literature, it was found that the integrated use of RS-based geospatial data with ML helps to produce an accurate result. Most of the authors used the regression algorithm in the estimation of the water quality parameters, with a support vector machine (SVM) regression intensively used. The artificial neural network (ANN) algorithm was the most used algorithm of ML in many of the studies. The researchers used multispectral images for their study. By applying ML to RS data, water quality monitoring systems are evolving into real-time artificial intelligence (AI)-enabled models that provide valuable recommendations and insights to support farmers to make decisions and take action in aquaculture.

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Water quality monitoring: from conventional to emerging technologies

Cited by 85 publications

References 13 publications

IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications

IoT Based Smart Water Quality Monitoring: Recent Techniques, Trends and Challenges for Domestic Applications

Deep learning-based production assists water quality warning system for reverse osmosis plants

Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data

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