Tools
Tools
Khan, W, Hussain, A, Alaskar, H, Baker, T, Ghali, F, Al-Jumeily, D and Al-Shamma’a, AI (2020) Prediction of Flood Severity Level Via Processing IoT Sensor Data Using Data Science Approach. IEEE Internet of Things Magazine, 3 (4). pp. 10-15. ISSN 2576-3180
|
Text
IOTMAG-19-00110.R1.pdf - Accepted Version Download (570kB) | Preview |
Abstract
The ‘riverine flooding’ is deemed a catastrophic phenomenon caused by extreme climate changes and other ecological factors (e.g., amount of sunlight), which are difficult to predict and monitor. However, the use of internet of things (IoT), various types of sensing including social sensing, 5G wireless communication and big data analysis have devised advanced tools for early prediction and management of distrust events. To this end, this paper amalgamates machine learning models and data analytics approaches along-with IoT sensor data to investigate attribute importance for the prediction of risk levels in flood. The paper presents three river levels: normal, medium and high-risk river levels for machine learning models. Performance is evaluated with varying configurations and evaluations setup including training and testing of support vector machine and random forest using principal components analysis-based dimension reduced dataset. In addition, we investigated the use of synthetic minority over-sampling technique to balance the class representations within dataset. As expected, the results indicated that a “balanced” representation of data samples achieved high accuracy (nearly 93%) when benchmarked with “imbalanced” data samples using random forest classifier 10-folds cross-validations
| 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 Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Computer Science & Mathematics Engineering |
| Publisher: | IEEE |
| Date Deposited: | 20 May 2020 11:32 |
| Last Modified: | 18 Aug 2022 10:45 |
| DOI or ID number: | 10.1109/IOTM.0001.1900110 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/12971 |
 |
View Item |