SLIIT Conference and Symposium Proceedings

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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.

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Subject: search.filters.subject.Flood prediction

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    Drown Prevention and Flood prediction using smart embedded devices
    (IEEE, 2019-12-05) Samarasinghe, D; De Silva, P. M; Mudalige, T. U; Gamage, M. K. I; Abeygunawardhana, P. K. W
    Drowning and Flood becomes major negative impact to the mankind and infrastructure. Drowning is caused by the when person go into deeper areas or else due to a person's health condition. Flood is a natural disaster commonly caused by the run of rivers due to excessively highly rainy season or due to environment effect or global warming effect. Hence IoT with sensor technology support us to efficiently cover up this impact for mankind. This research support for each mankind to survive from the drowning threat and this may help for people to survive from the natural disaster like flooding. This research presents two IoT Devices consisting with sensors and monitoring system to determine the flood level, the user condition and water level when user in the water. Then generating alert via the mobile application to notify the user. Machine learning algorithms were implemented to perform the level classification.
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    Early Warning for Pre and Post Flood Risk Management by Using IoT and Machine Learning
    (2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Ilukkumbure, S.P.M.K.W.; Samarasiri, V.Y.; Mohamed, M.F.; Selvaratnam, V.; Rajapaksha, U.U.S.
    Flooding has been a very treacherous situation in Sri Lanka. Therefore, developing a structure to forecast risky weather conditions will be a great aid for citizens who are affected from flood d isasters. I n t his s tudy, t he a uthors explore the use of Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), and crowdsourcing to provide insights into the development of the pre and post flood r isk management system as a solution to manage and mitigate potential flood risks. Machine learning and deep learning algorithms are used to predict upcoming flooding s ituations and r ainfall occurrences by using predicted weather information and historical data set of flood a nd r ainfall. Crowdsourcing i s u sed a s a n ovel method for identifying flood t hreatening a reas. Weather i nformation is gathered from citizens and it will help to build a procedure to notify the public and authorities of imminent flood risks. The IoT device tracks the real-time meteorological conditions and monitors continuously. The overall outcome showcases that machine learning models, deep learning algorithms, IoT and crowdsourcing information are equally contributing to predict and forecast risky weather conditions. The integration of the above components with machine learning techniques, together with the availability of historical data set, can forecast flood occurrences and disastrous weather conditions with above 0.70 accuracy in specific areas of Sri Lanka.