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

Shidrokh Goudarzi,Zenon Chaczko,Domenico Ciuonzo,Mohammad Hossein Anisi,Azri Azmi,Mohammad Abdollahi Azgomi,Seyed Ahmad Soleymani,Nazri Kama,Salwani Abdullah

doi:10.32604/cmc.2022.019550

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.

Highlights

Effective river flow prediction is required to reduce the damage caused by potential surges.Various techniques have been proposed such as surge forecasting, river training, real-time alerts, stormwater predictions, and emergency management [1]
The Group Method Data Handling (GMDH)-Particle Swarm Optimization (PSO) network was compared with earlier models such as DE [35], Genetic Algorithms (GAs) [36], and Artificial Neural Networks (ANNs) [37] and the results are presented
To validate the precision of the developed GMDH-PSO model, its performance was compared to the DE, GA, and ANN models

Summary

Introduction

The 5G network provides high peak data rates with low latency and massive network capacity that would be very useful in flood management. In this regard, a great deal of attention has been paid to the use of Wireless Sensor Network (WSN), one of the enabling technologies in 5G networks, for river monitoring and flow predictions. Given the multi-hop nature of WSNs, such failure could put an end to the whole routing process if the failed nodes are network bottlenecks. Such failures could result in poor Quality of Service (QoS) and/or increased energy consumption due to increased re-transmission of unsuccessful packets

Objectives

Results

Conclusion