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IoT-enabled Flood Severity Prediction via Ensemble Machine Learning Models

Khalaf, M, Alaskar, H, Hussain, AJ, Baker, T, Maamar, Z, Buyya, R, Liatsis, P, Khan, W, Tawfik, H and Al-Jumeily, D IoT-enabled Flood Severity Prediction via Ensemble Machine Learning Models. IEEE Access. ISSN 2169-3536 (Accepted)

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River flooding is a natural phenomenon that can have a devastating effect on human life and economic losses. There have been various approaches in studying river flooding; however, insufficient understanding and limited knowledge about flooding conditions hinder the development of prevention and control measures for this natural phenomenon. This paper entails a new approach for the prediction of water level in association with flood severity using the ensemble model. Our approach leverages the latest developments in the Internet of Things (IoT) and machine learning for the automated analysis of flood data that might be useful to prevent natural disasters. Research outcomes indicate that ensemble learning provides a more reliable tool to predict flood severity levels. The experimental results indicate that the ensemble learning using the Long-Short Term memory model and random forest outperformed individual models with a sensitivity, specificity and accuracy of 71.4%, 85.9%, 81.13%, respectively.

Item Type: Article
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 06 Apr 2020 08:17
Last Modified: 04 Sep 2021 07:31
URI: https://researchonline.ljmu.ac.uk/id/eprint/12650

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