Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1962
Title: Early Warning for Pre and Post Flood Risk Management by Using IoT and Machine Learning
Authors: Ilukkumbure, S. P. M. K. W
Samarasiri, V. Y
Mohamed, M. F
Selvaratnam, V
Rajapaksha, S, K
Keywords: Early Warning
Post Flood Risk Management
Using IoT
Machine Learning
Issue Date: 9-Dec-2021
Publisher: IEEE
Citation: S. P. M. K. W. Ilukkumbure, V. Y. Samarasiri, M. F. Mohamed, V. Selvaratnam and U. U. Samantha Rajapaksha, "Early Warning for Pre and Post Flood Risk Management by Using IoT and Machine Learning," 2021 3rd International Conference on Advancements in Computing (ICAC), 2021, pp. 252-257, doi: 10.1109/ICAC54203.2021.9671141.
Series/Report no.: 2021 3rd International Conference on Advancements in Computing (ICAC);Pages 252-257
Abstract: 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 disasters. In this study, the authors 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 risk management system as a solution to manage and mitigate potential flood risks. Machine learning and deep learning algorithms are used to predict upcoming flooding situations and rainfall occurrences by using predicted weather information and historical data set of flood and rainfall. Crowdsourcing is used as a novel method for identifying flood threatening areas. Weather information 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.
URI: http://rda.sliit.lk/handle/123456789/1962
ISBN: 978-1-6654-0862-2
Appears in Collections:Research Papers - IEEE
Research Papers - SLIIT Staff Publications
Research Publications -Dept of Information Technology

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