A rapid method based on computer vision for the analysis of hardness and eutrophication levels in water bodies

Sivaranjani, G.; https:, Doi:; Kumar, Ponnusamy Senthil; Gopinath, Kannappan Panchamoorthy

doi:10.5004/dwt.2018.22671

Cited by 2 publications

(2 citation statements)

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“…Total phosphate (T.P.) values were determined by the molybdenum blue method (Sivaranjani et al., 2018), and images were also captured. Several Ideal RGB color palettes and their measurement were obtained through the user interface from the calibration formed in the laboratory and made as a training set for machine learning as per APHA methods.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approach for determining the water quality of freshwater lakes: A case study on selected lakes of Chennai, India

Venkatesh,

Tharanikumar,

Ashokkumar

et al. 2023

Environmental Quality Mgmt

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Monitoring freshwater quality in urban like Chennai is indispensable as the chief drinking water source for the city originates from freshwater lakes. This study portrays an economical, user‐friendly, economical method to detect water quality. A machine learning‐based portable water quality check test kit was developed along with a user interface. To validate the efficiency of the portable kit, this study focussed on the ten major freshwater lakes of Chennai City. Basic quality parameters of water, such as the potential of hydrogen (pH), level of ammonia, nitrite, nitrate, and total phosphate (T.P.), were considered when designing this mobile phone‐compatible user interface application. The experiment used and tested water samples from ten different lakes with the commercially available freshwater master test kit. The images of color developed during the test were captured, and corresponding Red, Green, Blue (RGB) values were calculated. These values were used in Igray, Iavg, and Euclidean distance algorithms to predict the pollution level of the samples. Based on the models, the Euclidean model fitted well with the calibration data with a correlation value of more than 0.98 for all the parameters. Percent accuracy values of the methods used in this study were compared, and the Euclidean model showed more than 98% accuracy for all parameters. The result of this study is alarming that all the ten freshwater lakes considered in this study were hypereutrophic.

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Section: Methodsmentioning

confidence: 99%

Machine learning approach for determining the water quality of freshwater lakes: A case study on selected lakes of Chennai, India

Venkatesh,

Tharanikumar,

Ashokkumar

et al. 2023

Environmental Quality Mgmt

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show abstract

“…Correspondingly, nitrite, andnitrate tests were carried out prescribed by the kit. Phosphate tests were con-ducted by the molybdenum blue method defined bySivaranjani et al (2018).…”

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confidence: 99%

Machine learning approach for measuring water quality of coastline and estuaries in Chennai coastal area

Kamarajan¹,

Mohan²,

Shanmugam³

et al. 2023

Environmental Quality Mgmt

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Chennai has had the longest coastline over other major cities of India. It is decidedly vital to monitor seawater quality due to the increased coastline population. This study presents an Android mobile application based on a machine learning approach to perform basic testing parameters of seawater by applying convolutional neural network concepts. A commercially available “saltwater master test kit” was used in this study to test the level of pH, Ammonia, Nitrite, Nitrate, and Total Phosphate (T.P.) in seawater. Six water samples collected from every 10 regions, including the coastline and coastal estuaries, were tested with the test kit in a microplate. Images were captured in the solely designed mobile app, and they were pre‐processed, and RGB (Red, Green, and Blue) were recorded from the Region of Interest (R.O.I.) of the image. A supervised Convolutional Neural Network Image Classifier (SCNNIC) algorithm was developed to classify the RGB pixel values. CIEDE2000 (C2K) color difference algorithm was applied over the recorded RGB values with the datasets stored previously to result in the nearest color match between the ideal dataset and R.O.I. of the captured image. Grayscale and RGB methods results were compared with the standard APHA method. This C2K color difference algorithm produced a percent accuracy of over 98% compared with other methods used, and R2 recorded by curve fitting method for pH, T.N. and T.P. were above 0.98. Disquieting results were reported in this study, especially in Muttukkadu backwater and Adayar river backwater estuaries, reported high values of pH (7.82 and 8.17), TN (13.74 mg/L and 13.45 mg/L) and T.P. (0.266 μg/L and 0.724 μg/L). The mean for all 10 regions of 110 km chosen in this study for the 2 years was calculated, and values were obtained as pH‐8.33, TN‐5.321 mg/L, and TP‐0.143 μg/L.

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A rapid method based on computer vision for the analysis of hardness and eutrophication levels in water bodies

Cited by 2 publications

References 0 publications

Machine learning approach for determining the water quality of freshwater lakes: A case study on selected lakes of Chennai, India

Machine learning approach for determining the water quality of freshwater lakes: A case study on selected lakes of Chennai, India

Machine learning approach for measuring water quality of coastline and estuaries in Chennai coastal area

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