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

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 50 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 (R-2), correlation coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and BIAS are provided.

Automated summary

This article proposes a fault-tolerant multi-level framework that integrates unmanned aerial vehicles (UAVs) and wireless sensor networks (WSN) for river level monitoring and flood prediction. A water-level prediction model based on a hybrid algorithm of Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is introduced and trained using real dataset. The performance of the proposed model is evaluated and compared with other models using different metrics, showing good results.