Multiple linear regression based water quality parameter modeling to detect hexavalent chromium in drinking water

Krishnan, K. S.; Bhuvaneswari, P. T. V.

doi:10.1109/wispnet.2017.8300197

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…A variety of statistical models are also used in water quality parameter estimation including multiple linear regression [24]. Abyaneh [25] compared the performance between statistical models (e.g., multivariate linear regression model) and ANN models in estimating the biochemical oxygen demand (BOD) and chemical oxygen demand (COD), which shows ANN performs better.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation

Wang

Chen

et al. 2022

IEEE Internet Things J.

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Deteriorating water quality leads to the freshwater biodiversity crisis. The interrelationships among water quality parameters and the relationships between these parameters and taxa groups are complicated in affecting biodiversity. Nevertheless, due to the limited types of Internet of Things (IoT) sensors available on the market, a large number of chemical and biological parameters still rely on laboratory tests. With the latest advancement in artificial intelligence and the IoT (AIoT), this technique can be applied to real-time monitoring of water quality, and further conserving biodiversity. In this paper, we conducted a comprehensive literature review on water quality parameters that impact the biodiversity of freshwater and identified the top-10 crucial water quality parameters. Among these parameters, the interrelationships between the IoT measurable parameters and IoT unmeasurable parameters are estimated using a general regression neural network model and a multivariate polynomial regression model based on historical water quality monitoring data. Conventional field water sampling and in-lab experiments, together with the developed IoT-based water quality monitoring system were jointly used to validate the estimation results along an urban river in Hong Kong. The general regression neural network model can successfully distinguish the abnormal increase of parameters against normal situations. For the multivariate polynomial regression model of degree eight, the coefficients of determination results are 0.89, 0.78, 0.87, and 0.81 for NO3-N, BOD5, PO4, and NH3-N, respectively. The effectiveness and efficiency of the proposed systems and models were validated against laboratory results and the overall performance is acceptable with most of the prediction errors smaller than 0.2mg/L, which provides insights into how AIoT techniques can be applied to pollutant discharge monitoring and other water quality regulatory applications for freshwater biodiversity conservation.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation

Wang

Chen

et al. 2022

IEEE Internet Things J.

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“…Multiple linear regression is used to assess each variable's impact on the output when numerous input variables are present. Equation 2is the mathematical representation of the multiple linear regression analysis where y refers to the output of the applied machinelearning algorithm, x is the obtained values, β is the slope on the curve, and e is the error coefficient [112].…”

Section: Multiple Linear Regressionmentioning

confidence: 99%

Recent Advancement of the Sensors for Monitoring the Water Quality Parameters in Smart Fisheries Farming

et al. 2021

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Water quality is the most critical factor affecting fish health and performance in aquaculture production systems. Fish life is mostly dependent on the water fishes live in for all their needs. Therefore, it is essential to have a clear understanding of the water quality requirements of the fish. This research discusses the critical water parameters (temperature, pH, nitrate, phosphate, calcium, magnesium, and dissolved oxygen (DO)) for fisheries and reviews the existing sensors to detect those parameters. Moreover, this paper proposes a prospective solution for smart fisheries that will help to monitor water quality factors, make decisions based on the collected data, and adapt more quickly to changing conditions.

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“…Jalal and Ezzedine [23] proposed water monitoring system that uses a new generation of wireless sensors to detect the chemical, physical and microbiological water parameters in Tunisian water resources. The proposed system in [24] examine the relationship between pH, total dissolved solids (TDS) and conductivity water quality parameters (WQPs) involved in the detection of hexavalent chromium contamination in the drinking water distribution system. Finally, Zennaro M et al [25] present the design of a water quality measuring system and propose a prototype implementation of a water quality wireless sensor network (WQWSN) as a solution to water quality measurement in Malawi.…”

Section: Telkomnikatelecommun Comput El Controlmentioning

confidence: 99%

Design and implementation of remotely monitoring system for pH level in Baghdad drinking water networks

Mohammed

Ismail

2021

TELKOMNIKA

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Many people in the recent days have suffering from number of diseases due to unsafe and impure drinking water, especially in rural areas. As typical laboratory experiments and official water quality tests take considerable amount of time to obtain results and due to non availability of a simple device that can measure such water quality parameters in real time, therefore in this paper a remote pH level monitoring system for Baghdad drinking water system is proposed. A PH level sensing and monitoring nodes are distributed at different location. These nodes are proactively measured pH level of water and send data to the maintenance center to give them overall picture about pH level via global position system (GSM). This proposed system gives a robust, low-cost and effective method for the drinking water maintenance center to measure and monitoring the water quality in real time environment.

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Multiple linear regression based water quality parameter modeling to detect hexavalent chromium in drinking water

Cited by 9 publications

References 3 publications

Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation

Real-Time Water Quality Monitoring and Estimation in AIoT for Freshwater Biodiversity Conservation

Recent Advancement of the Sensors for Monitoring the Water Quality Parameters in Smart Fisheries Farming

Design and implementation of remotely monitoring system for pH level in Baghdad drinking water networks

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