Integrated online water quality monitoring

Wiranto, Goib; Maulana, Yudi Yuliyus; Hermida, I Dewa Putu; Syamsu, Iqbal; Mahmudin, Dadin

doi:10.1109/icssa.2015.7322521

Cited by 17 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Das and Jain [ 121 ] 2017 Zigbee,GSM pH, EC and Temp Jindal et al [ 54 ] 2017 Bluetooth/Wi-Fi Temp, DO and EC Kamaludin and Ismail [ 59 ] 2017 RFID pH Khaleeq et al [ 122 ] 2017 Wi-Fi pH, EC and Temp Parameswari and Moses [ 62 ] 2018 Wi-Fi Temp, pH, EC and TUR Kafli and Isa [ 123 ] 2017 GSM Temp, humidity, CO and pH Salim et al [ 124 ] 2017 Wi-Fi Temp, pH, DO, TUR, EC, TDS and Salinity Myint et al [ 125 ] 2017 Zigbee pH, TUR, CO2 and Temp Cloete et al [ 126 ] 2016 Zigbee pH, EC, Temp and ORP Raut and Shelke [ 127 ] 2016 Zigbee pH, TUR Ionel et al [ 66 ] 2015 GPRS Nitrate (NO 3− ), copper(Cu ++ ), chloride(Cl − ). Wiranto et al [ …”

Section: Resultsmentioning

confidence: 99%

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Essamlali,

Nhaila,

El Khaili

2024

Heliyon

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Essamlali,

Nhaila,

El Khaili

2024

Heliyon

View full text Add to dashboard Cite

“…This work studied the feasibility of a new water quality sensor for RAS, based on the record and prediction of toxicity by us of linear statistical models and a whole-cell biosensor. Over the years, the industry and academia have been focused on the use of wireless sensor nodes employing electronic and electrochemical sensors [ 31 , 32 , 33 ]. These systems focus not only in monitoring the water quality of the RAS system, but also provide control of the system during normal operation through environmental sensors and feed and also provide an alarm, if needed.…”

Section: Resultsmentioning

confidence: 99%

Monitoring Aquaculture Water Quality: Design of an Early Warning Sensor with Aliivibrio fischeri and Predictive Models

Silva

Yang

Pires

et al. 2018

Sensors

View full text Add to dashboard Cite

A novel toxicity-warning sensor for water quality monitoring in recirculating aquaculture systems (RAS) is presented. The design of the sensor system mainly comprises a whole-cell biosensor. Aliivibrio fischeri, a luminescent bacterium widely used in toxicity analysis, was tested for a mixture of known fish-health stressors, namely nitrite, un-ionized ammonia, copper, aluminum and zinc. Two toxicity predictive models were constructed. Correlation, root mean squared error, relative error and toxic behavior were analyzed. The linear concentration addition (LCA) model was found suitable to ally with a machine learning algorithm for prediction of toxic events, thanks to additive behavior near the limit concentrations for these stressors, with a root-mean-squared error (RMSE) of 0.0623, and a mean absolute error of 4%. The model was proved to have a smaller relative deviation than other methods described in the literature. Moreover, the design of a novel microfluidic chip for toxicity testing is also proposed, which is to be integrated in a fluidic system that functions as a bypass of the RAS tank to enable near-real time monitoring. This chip was tested with simulated samples of RAS water spiked with zinc, with an EC50 of 6,46E-7 M. Future work will be extended to the analysis of other stressors with the novel chip.

show abstract

“…There are dozens of IoT-based water monitoring systems that have been proposed recently for different areas of applications for river and seawater monitoring [1], [2], [3]. Some of them are actively used for fish farms and aquaculture centers [4], [5]; others are used for monitoring drinking water distribution systems [6] and contamination in drinking water [7]. The detailed surveys on the approaches, tools and techniques employed in existing IoT-based water quality monitoring solutions can be found in [8] and [9].…”

Section: Related Workmentioning

confidence: 99%

Emerging Tools for Design and Implementation of Water Quality Monitoring Based on IoT

Skarga-Bandurova

Velykzhanin

Barbaruk

et al. 2020

CSIMQ

View full text Add to dashboard Cite

The article provides a conceptual framework for developing real-time water monitoring systems based on IoT technology. The process, strategy and knowledge base for multidisciplinary research on IoT systems and prerequisites for real-world application of IoT technology into continuous water quality monitoring are discussed. The study expands current efforts aimed at leveraging customized IoT solutions for better instrumentation and continued integration of sensor data into networks. The process of system design from scratch and base components of IoT-based water quality monitoring systems for surface water are described. While the focus of this article is on system design, opportunities to improve the system components for the management of water resources with continuous water quality monitoring are much broader. In this view, perspectives and development issues of IoT-based water quality monitoring are also discussed.

show abstract

Integrated online water quality monitoring

Cited by 17 publications

References 3 publications

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Monitoring Aquaculture Water Quality: Design of an Early Warning Sensor with Aliivibrio fischeri and Predictive Models

Emerging Tools for Design and Implementation of Water Quality Monitoring Based on IoT

Contact Info

Product

Resources

About