A Novel Neural Network-Based Identification of Flood Regions Using UAV Images - Kumar Jangid, Pradeep; Gupta, Namit; Chandani, Priyanka; G., Shanmugarathinam (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Countless lives have been lost, and numerous buildings and other economic assets have been ruined, all because of floods. People caught in flood zones have no way to go to safety since their houses and other structures have been destroyed along with vital infrastructure. In order to preserve lives, property, and essential city services, it is crucial to create systems that may identify floods in an area and provide help and assistance to those in need as soon as possible. Current methods of detecting floods and damage assessment make use of remote sensing, satellite imaging, GPS systems, and geospatial databases. These strategies use neural networks, machine learning, and deep learning. In light of this, this research employs aerial data captured by unmanned aerial vehicles (UAVs) as part of a Genetic Bilateral Convolutional Neural Network, also known as (GBCNN)-based flood detection algorithm, in order to obtain flood-related characteristics from photos of the disaster area. This technique helps determine the extent of destruction to neighborhood facilities in disaster areas. The research uses UAV imagery gathered before and after a flood in a flood-prone zone. These swaths of the training dataset teach the GBCNN model to identify and extract the places where flooding-related changes have occurred. The model is validated by comparing it to photographs taken before and after a catastrophe; the findings show that it can correctly identify floods 91% of the time. This model may be used by organizations that deal with disaster management to evaluate the extent of damage to essential municipal facilities and other assets throughout the globe. This may aid in the wise administration of cities, ensuring that sudden calamities are dealt with immediately.