AI-Aided Robotic Wide-Range Water Quality Monitoring System

Ameen Awwad (), Ghaleb A. Husseini and Lutfi Albasha
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Ameen Awwad: Department of Electrical Engineering, American University of Sharjah, University City, Sharjah P.O. Box 26666, United Arab Emirates
Ghaleb A. Husseini: Department of Chemical and Biological Engineering, American University of Sharjah, University City, Sharjah P.O. Box 26666, United Arab Emirates
Lutfi Albasha: Department of Electrical Engineering, American University of Sharjah, University City, Sharjah P.O. Box 26666, United Arab Emirates

Sustainability, 2024, vol. 16, issue 21, 1-14

Abstract: Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria ( A. baumanii , E. coli , and P. aeruginosa ) and harmless ones found in drinking water and wastewater ( E. faecium , L. paracasei , and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%.

Keywords: automation; airborne surveying; deep neural networks; image processing; microscopic images; morphology; optimization algorithm; risk assessment; safe water supply; waterborne diseases; water quality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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