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Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

Goudarzi, Shidrokh and Ahmad Soleymani, Seyed and Anisi, Mohammad Hossein and Ciuonzo, Domenico and Kama, Nazri and Abdullah, Salwani and Abdollahi Azgomi, Mohammad and Chaczko, Zenon and Azmi, Azri (2021) 'Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network.' Computers, Materials and Continua, 70 (1). 715 - 738. ISSN 1546-2218

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Abstract

The Wireless Sensor Network (WSN) is a promising technology that could be used to monitor rivers’ water levels for early warning flood detection in the 5G context. However, during a flood, sensor nodes may be washed up or become faulty, which seriously affects network connectivity. To address this issue, Unmanned Aerial Vehicles (UAVs) could be integrated with WSN as routers or data mules to provide reliable data collection and flood prediction. In light of this, we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river levels. The framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a flood. Besides, an algorithm hybridized with Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is proposed to predict forthcoming floods in an intelligent collaborative environment. The proposed water-level prediction model is trained based on the real dataset obtained from the Selangor River in Malaysia. The performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination (), correlation coefficient (), Root Mean Square Error (), Mean Absolute Percentage Error (), and are provided.

Item Type: Article
Uncontrolled Keywords: Unmanned aerial vehicles; wireless sensor networks; group method data handling; particle swarm optimization; river flow; prediction
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 21 Sep 2021 09:44
Last Modified: 21 Sep 2021 09:44
URI: http://repository.essex.ac.uk/id/eprint/31133

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