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

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

doi:10.32604/cmc.2022.019550

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…The root means square error (RMSE) can be an effective metric for assessing the accuracy of the scheme's performance [14]. To this end, we consider the actual position of the target and the estimated position of the target by the UAVs.…”

Section: Numerical Resultsmentioning

confidence: 99%

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

Soleymani,

Goudarzi,

Liu

et al. 2023

2023 Sensor Signal Processing for Defence Conference (SSPD)

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This paper proposes a scheme for multiple unmanned aerial vehicles (UAVs) to track multiple targets in challenging 3-D environments while avoiding obstacle collisions. The scheme relies on Received-Signal-Strength-Indicator (RSSI) measurements to estimate and track target positions and uses a Q-Learning (QL) algorithm to enhance the intelligence of UAVs for autonomous navigation and obstacle avoidance. Considering the limitation of UAVs in their power and computing capacity, a global reward function is used to determine the optimal actions for the joint control of energy consumption, computation time, and tracking accuracy. Extensive simulations demonstrate the effectiveness of the proposed scheme, achieving accurate and efficient target tracking with low energy consumption.

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Section: Numerical Resultsmentioning

confidence: 99%

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

Soleymani,

Goudarzi,

Liu

et al. 2023

2023 Sensor Signal Processing for Defence Conference (SSPD)

Self Cite

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“…Previous literature presents several models using the AIoT as an efficient means of real-time monitoring of river levels, and of providing predictions on eventual river floods. Goudarzi et al [ 46 ] proposed a multilevel architecture where WSNs are backed by Unmanned Aerial Vehicles (UAV) that can act in case of connectivity issues. The collected data is fed to a Particle Swarm Optimization (PSO) algorithm that will predict forthcoming floods.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Boulouard

Ouaissa

et al. 2022

Sensors

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River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.

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“…Goudarzi Shidrokh used a fault-tolerant multi-level framework consisting of wireless sensor networks and unmanned aerial vehicles (UAVs) to monitor river water levels, and proposed an algorithm that combines group method data processing and particle swarm optimization to predict impending flood disasters in an intelligent collaborative environment. Experimental analysis showed that the proposed water level prediction model had high accuracy [9]. Although existing monitoring and prediction methods have certain accuracy in flood and landslide disaster prevention, the monitoring data obtained from them still cannot meet the needs of dynamic and intelligent monitoring and analysis of flood and landslide disasters.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Based Flood and Landslide Disaster Monitoring and Dynamic Numerical Prediction System

Fajing Wang, Shuantao Dong, Xu Feng

2024

jes

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Monitoring and dynamic numerical prediction of flood and landslide disasters are of great significance for reducing disaster risks, ensuring urban safety and stable development. The existing systems have limitations in real-time and dynamic performance, making it difficult to provide effective support for disaster prevention and management. To improve the efficiency of disaster monitoring, this article combined artificial intelligence (AI) to conduct in-depth research on the construction of flood and landslide disaster monitoring and dynamic numerical prediction systems. This article first analyzed the system functional requirements, and then designed the system architecture based on this, dividing it into three levels: user interface layer, application layer, and data layer. Finally, the BP (Back Propagation) neural network algorithm and dynamic numerical model were utilized to analyze the monitoring and dynamic numerical prediction of flood and landslide disasters. To verify the effectiveness of the system, this article compared it with GIS (Geographic Information System) based methods, and conducted testing and analysis on the system from monitoring errors, real-time monitoring, numerical simulation, and several levels. The results showed that in terms of real-time monitoring, compared with GIS based disaster prediction systems, the average response time of the AI based disaster monitoring and prediction system in this paper was shortened by 1.36 milliseconds. The conclusion indicated that the AI-based flood and landslide disaster monitoring and dynamic numerical prediction system in this article could effectively improve the efficiency of disaster monitoring and early warning, and help promote the efficient and intelligent development of disaster prevention

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

Cited by 9 publications

References 38 publications

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention

Artificial Intelligence Based Flood and Landslide Disaster Monitoring and Dynamic Numerical Prediction System

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