Open Access

ARTICLE

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

Shidrokh Goudarzi^1,2,*, Seyed Ahmad Soleymani^2,3, Mohammad Hossein Anisi⁴, Domenico Ciuonzo⁵, Nazri Kama⁶, Salwani Abdullah¹, Mohammad Abdollahi Azgomi², Zenon Chaczko⁷, Azri Azmi⁶

1 Centre for Artificial Intelligent (CAIT), Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
2 School of Computer Engineering, Iran University of Science and Technology, Resalat Sq., 16846-13114, Tehran, Iran
3 School of Computing Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Malaysia
4 School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
5 Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, Naples, 80125, Italy
6 Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, Malaysia
7 School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW, Australia

* Corresponding Author: Shidrokh Goudarzi. Email:

Computers, Materials & Continua 2022, 70(1), 715-738. https://doi.org/10.32604/cmc.2022.019550

Received 16 April 2021; Accepted 18 May 2021; Issue published 07 September 2021

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.

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