Faculty of Computing

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Subject: search.filters.subject.WQI

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    E-tongue -A smart tool to predict safe consumption of groundwater
    (IEEE, 2020) Alahakoon, A. M. P. B; Nibraz, M. M; Gunarathna, P. M. S. S. B; Thenuja, S; Kahandawaarchchi, K. A. D. C. P; Gamage, N. D. U
    — In Sri Lanka, reportedly 59% of the population depends on water from natural sources. The government has taken necessary action to provide a quality water, there has always been a need to educate people about the importance of maintaining water quality, the importance of using betterquality water, and necessary precautions to be taken to avoid the Chronic Kidney Disease (CKD). Prior studies of the problems that has to induce to implement an E-Tongue: a smart device to predict safe consumption of groundwater, which is identify the quality of a groundwater in real-time by designing an Internet of Things (IoT) device to read the value of water quality parameters and GPS to fetch location which will be then transferred to cloud environment for an easy access by the machine learning model to process and identify the Water Quality Index (WQI). It will then predict the water quality parameter levels that could be changed in the future and check the possibility of CKD. All the outputs will be finally displayed via the mobile application with 73% accuracy.
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    Water Quality Index Based Prediction of Ground Water Properties for Safe Consumption
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Alahakoon, A.M.P.B.; Nibraz, M.M.; Gunarathna, P.M.S.S.B.; Thenuja, T.; Kahandawaarchchi Faculty of Computing Sri Lanka Institute of Information Technology, K.A.D.C.P. Malabe, Sri Lanka; Gamage, N.D.U.
    Water as one crucial element for the survival of human beings is necessary to be handled with care. With a 59% of the Sri Lankan population depending themselves on this indispensable element, the authorities and the people must heed the importance of safe consumption to avoid severe consequences like Chronic Kidney Disease (CKD). In an attempt to address this social predicament, a smart device was fashioned as an initiative to predict safe consumption of groundwater and, to oblige and upskill the users to identify the quality of a groundwater sample in real-time. With the inclusion of machine learning techniques, the implementation was done by predicting the Water Quality Index (WQI) which is a single numeric index that mirrors the overall quality of any water sample, with an accuracy of 97.82 % . In addition, two more serviceable functionalities to predict possibilities of CKD outbreak and forecasting water quality parameters were also implemented with accuracies of 76.99% and 92% respectively. The sole of this research relies on the hardware device that embeds a set of sensors which accompanies the individual functionalities. The readings and outputs will be displayed through the mobile application which is real-time and of high performance with a friendly user-interface.