Browsing by Author "Gamage, N.D.U."

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Proximity based Intelligent Air Pollution Alerts for Garbage Disposal Sites
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wijerathne, W.G.D.U.; Perera, M.L.M.P.; Nuwandika, R.H.C.; Ranasinghe, R.A.K.A.; Kahandawaarachchi, K.A.D.C.P.; Gamage, N.D.U.
Air pollution is one of the key trending challenges faced by the public at present. The garbage disposal sites are the major contributors which emit harmful gases (CO, CO2, CH4) where toxicity is at a higher level. This research attempts to fill the lacuna by providing an intelligent proximity-based air pollution detection system that alerts and makes the public aware of the danger and risk of the garbage dumps that are located near them via a mobile application. The device is developed to detect harmful gas with MG811, MQ7, MQ4 sensors with 0.80 accuracy. The device also contains a DHT22 temperature humidity sensor with 0.80 accuracy and SD011 particulate matter sensor which has a 0.95 accuracy. The application can notify the responsible authorities regarding the possible risks of the garbage dump and the health effects that can cause. The air toxicity is calculated with an accuracy of 0.75 and visualized, using the landfill classification and pollution level prediction algorithms with an accuracy of 0.96 in a geo proximity map with 0.92 percent accuracy.
Embargo
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.