Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

Saboe, Daniel; Ghasemi, Hamidreza; Gao, Ming Ming; Samardzic, Mirjana; Hristovski, Kiril; Boscovic, Dragan; Burge, Scott R.; Burge, Russell G.; Hoffman, David

doi:10.1016/j.scitotenv.2020.142876

Cited by 49 publications

(17 citation statements)

References 9 publications

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“…In addition, algal blooms contain chlorophyll-a which was identified using satellite imagery to explore the spatial distribution of the pollutants [176]. The latest sensor to be applied in the analysis of water quality dynamics is a bluegreen algae indicator [179]. The other biological indicator is phytoplankton [151].…”

Section: Biological Elementsmentioning

confidence: 99%

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

2023

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The complex interactions from anthropogenic activities, climate change, sedimentation and the input of wastewater has significantly affected the aquatic environment and entire ecosystem. Over the years, the researchers have investigated water monitoring approaches in terms of traditional monitoring or even integrated systems to handle such an environmental assessment and predictions based on warning systems. However, research into the selection and optimisation of water monitoring systems by the combination of parallel approach in terms of sampling techniques, process analysis and results is limited. The research objectives of the present study are to evaluate the existing water monitoring systems based on the latest approach and then provide insights into factors affecting sensor implementation at sampling locations. Here we summarize the advancement and trends of various water monitoring systems as well as the suitability of sensor placement in the area by reviewing more than 300 papers published between 2011 and 2022. The research highlights the urgency of an integrative approach with regard to water monitoring systems including water quality model and water quantity model. A framework is proposed to incorporate all water monitoring approaches, sampling techniques, and predictive models to provide comprehensive information about environmental assessment. It was observed that the urgency of model-based approaches as verification and fusion of data assemble has the ability to improve the performances of the systems. Furthermore, integrated systems with the inclusion of a separate modeling approach through integrated -semi-mechanistic models, data science and artificial intelligence are recommended in the future. Overall, this study provides guidelines for achieving standardized water management by implementing integrated water monitoring systems INDEX TERMS infrastructure, integrated monitoring, water monitoring system, modelling, water quality

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Section: Biological Elementsmentioning

confidence: 99%

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

2023

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“…They permeate our daily lives; people use popular smart devices and services such as Alexa, Amazon, Google Maps, and smart wearable gadgets even without realizing, sometimes, how they have been built. ML and AI applications are revolutionizing the productivity and workflow of several fields throughout the world, such as healthcare, 1,2 education, 3,4 transportation and road safety, 5–7 farming and agriculture, 8,9 smart energy and manufacturing, 10,11 clean environment and waste management, 12,13 crime detection and policing, 14,15 finance, 16,17 pandemic management such as COVID-19, 18,19 water quality and management, 20,21 and many more. The data-driven and AI-enabled solutions have significantly increased the capacity to interpret enormous volumes of data created in today’s sensor-enabled environment.…”

Section: Introductionmentioning

confidence: 99%

The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights

Maabreh

Qolomany

et al. 2022

International Journal of Distributed Sensor Networks

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Despite the encouraging outcomes of machine learning and artificial intelligence applications, the safety of artificial intelligence–based systems is one of the most severe challenges that need further exploration. Data set poisoning is a severe problem that may lead to the corruption of machine learning models. The attacker injects data into the data set that are faulty or mislabeled by flipping the actual labels into the incorrect ones. The word “robustness” refers to a machine learning algorithm’s ability to cope with hostile situations. Here, instead of flipping the labels randomly, we use the clustering approach to choose the training samples for label changes to influence the classifiers’ performance and the distance-based anomaly detection capacity in quarantining the poisoned samples. According to our experiments on a benchmark data set, random label flipping may have a short-term negative impact on the classifier’s accuracy. Yet, an anomaly filter would discover on average 63% of them. On the contrary, the proposed clustering-based flipping might inject dormant poisoned samples until the number of poisoned samples is enough to influence the classifiers’ performance severely; on average, the same anomaly filter would discover 25% of them. We also highlight important lessons and observations during this experiment about the performance and robustness of popular multiclass learners against training data set–poisoning attacks that include: trade-offs, complexity, categories, poisoning resistance, and hyperparameter optimization.

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“…have received a great deal of attention from research communities. ,− Many biosensing technologies (quadrupole impedance conversion technique, self-referencing micro-optrode technique, microcontroller-based monitoring device, miniaturized surface acoustic wave, surface plasmon resonance, surface-enhanced Raman spectroscopy devices, electrochemical and acoustic immunosensors, enzymatic and DNA biosensors, etc.) are developed for water quality monitoring and assessments. ,− Recent advanced technologies (machine learning tools and artificial intelligence technology) were also adopted for real-time water quality monitoring . Wireless sensor technology for real-time water quality monitoring is an interesting topic among environmental researchers because this tool is ideally reliable, automatic, computer-based, and miniaturized and utilizes less power .…”

Section: Introductionmentioning

confidence: 99%

“…2,11−15 Recent advanced technologies (machine learning tools and artificial intelligence technology) were also adopted for real-time water quality monitoring. 16 Wireless sensor technology for real-time water quality monitoring is an interesting topic among environmental researchers because this tool is ideally reliable, automatic, computer-based, and miniaturized and utilizes less power. 17 Bioaccumulation, toxicity, and ecosystem monitoring are different types of bioresponse monitoring for water quality.…”

Section: Introductionmentioning

confidence: 99%

IR-Based Novel Device for Real-Time Online Acquisition of Fish Heart ECG Signals

Ren

Poopal

et al. 2022

Environ. Sci. Technol.

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We developed an infrared (IR)-based real-time online monitoring device (US Patent No: US 10,571,448 B2) to quantify heart electrocardiogram (ECG) signals to assess the water quality based on physiological changes in fish. The device is compact, allowing us to monitor cardiac function for an extended period (from 7 to 30 days depending on the rechargeable battery capacity) without function injury and disturbance of swimming activity. The electrode samples and the biopotential amplifier and microcontroller process the cardiac-electrical signals. An infrared transceiver transmits denoised electrocardiac signals to complete the signal transmission. The infrared receiver array and biomedical acquisition signal processing system send signals to the computer. The software in the computer processes the data in real time. We quantified ECG indexes (P-wave, Q-wave, R-wave, S-wave, T-wave, PR-interval, QRS-complex, and QT-interval) of carp precisely and incessantly under the different experimental setup (CuSO4 and deltamethrin). The ECG cue responses were chemical-specific based on CuSO4 and deltamethrin exposures. This study provides an additional technology for noninvasive water quality surveillance.

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Real-time monitoring and prediction of water quality parameters and algae concentrations using microbial potentiometric sensor signals and machine learning tools

Cited by 49 publications

References 9 publications

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

Surface Water Monitoring Systems—The Importance of Integrating Information Sources for Sustainable Watershed Management

The robustness of popular multiclass machine learning models against poisoning attacks: Lessons and insights

IR-Based Novel Device for Real-Time Online Acquisition of Fish Heart ECG Signals

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