A Novel Neural Network-Based Identification of Flood Regions Using UAV Images

Authors

  • Pradeep Kumar Jangid Assistant Professor, Department of Electrical Engineeing, Vivekananda Global University, Jaipur, India
  • Namit Gupta Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Priyanka Chandani Associate Professor and HOD, Department of Data Science (DS), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Shanmugarathinam G. Professor & HOD, Department of Computer Science and Engineering, Presidency University, Bangalore, India,

Keywords:

Flood detection, Disaster management, UAV, Aerial imagery, Genetic Bilateral convolutional neural network (GBCNN).

Abstract

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.

Downloads

Download data is not yet available.

References

Islam, M.A., Rashid, S.I., Hossain, N.U.I., Fleming, R. and Sokolov, A., 2023. An integrated convolutional neural network and sorting algorithm for image classification for efficient flood disaster management. Decision Analytics Journal, 7, p.100225.

Khouakhi, A., Zawadzka, J. and Truckell, I., 2022. The need for training and benchmark datasets for convolutional neural networks in flood applications. Hydrology Research, 53(6), pp.795-806.

Hernández, D., Cecilia, J.M., Cano, J.C. and Calafate, C.T., 2022. Flood detection using real-time image segmentation from unmanned aerial vehicles on an edge-computing platform. Remote Sensing, 14(1), p.223.

Liu, B., Ayaz, F., Anh, H.D., Edwards, O., Zeng, Y., Sheng, Z., Duan, X. and Tian, D., 2023, March. UAV Integrated Internet-of-Vehicle (IoV) System for Flooding Monitoring and Rescue. In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022) (pp. 1004-1014). Singapore: Springer Nature Singapore.

Munawar, H.S., Ullah, F., Qayyum, S., Khan, S.I. and Mojtahedi, M., 2021. UAVs in disaster management: Application of integrated aerial imagery and convolutional neural network for flood detection. Sustainability, 13(14), p.7547.

Shubhasree, A.V., Sankaran, P. and Raghu, C.V., 2022, August. UAV Image Analysis of Flooded Area Using Convolutional Neural Networks. In 2022 International Conference on Connected Systems & Intelligence (CSI) (pp. 1-7). IEEE.

Haq, M.A., 2022. CNN-Based Automated Weed Detection System Using UAV Imagery. Comput. Syst. Sci. Eng., 42(2), pp.837-849.

Munawar, H.S., Hammad, A., Ullah, F. and Ali, T.H., 2019, December. After the flood: A novel application of image processing and machine learning for post-flood disaster management. In Proceedings of the 2nd International Conference on Sustainable Development in Civil Engineering (ICSDC 2019), Jamshoro, Pakistan (pp. 5-7).

Wang, Z.F., Yu, Y.F., Wang, J., Zhang, J.Q., Zhu, H.L., Li, P., Xu, L., Jiang, H.N., Sui, Q.M., Jia, L. and Chen, J.P., 2022. Convolutional neural-network-based automatic dam-surface seepage defect identification from thermograms collected from UAV-mounted thermal imaging camera. Construction and Building Materials, 323, p.126416.

Seydi, S.T., Saeidi, V., Kalantar, B., Ueda, N., van Genderen, J.L., Maskouni, F.H. and Aria, F.A., 2022. Fusion of the multisource datasets for flood extent mapping based on ensemble convolutional neural network (CNN) model. Journal of Sensors, 2022.

Gebrehiwot, A., Hashemi-Beni, L., Thompson, G., Kordjamshidi, P. and Langan, T.E., 2019. Deep convolutional neural network for flood extent mapping using unmanned aerial vehicles data. Sensors, 19(7), p.1486.

Kharazi, B.A. and Behzadan, A.H., 2021. Flood depth mapping in street photos with image processing and deep neural networks. Computers, Environment and Urban Systems, 88, p.101628.

Cirneanu, A.L. and Popescu, D., 2019. Flooded Area Detection By Gabor Filtering Based On Convolutional Neural Network. University Politehnica of Bucharest Scientific Bulletin Series C-Electrical Engineering and Computer Science, 81(1), pp.69-80.

Guha, S., Jana, R.K. and Sanyal, M.K., 2022. Artificial neural network approaches for disaster management: A literature review (2010–2021). International Journal of Disaster Risk Reduction, p.103276.

Rahnemoonfar, M., Murphy, R., Miquel, M.V., Dobbs, D. and Adams, A., 2018, July. Flooded area detection from UAV images based on densely connected recurrent neural networks. In IGARSS 2018-2018 IEEE international geoscience and remote sensing symposium (pp. 1788-1791). IEEE.

Patil, S. D. ., & Deore, P. J. . (2023). Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 230–236. https://doi.org/10.17762/ijritcc.v11i2s.6087

Thompson, A., Walker, A., Rodriguez, C., Silva, D., & Castro, J. Machine Learning Approaches for Sentiment Analysis in Social Media. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/153

Timande, S., Dhabliya, D. Designing multi-cloud server for scalable and secure sharing over web (2019) International Journal of Psychosocial Rehabilitation, 23 (5), pp. 835-841.

Downloads

Published

04.11.2023

How to Cite

Kumar Jangid, P. ., Gupta, N. ., Chandani, P. ., & G., S. . (2023). A Novel Neural Network-Based Identification of Flood Regions Using UAV Images. International Journal of Intelligent Systems and Applications in Engineering, 12(3s), 369–377. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3716

Issue

Vol. 12 No. 3s (2024)

Section

Research Article

License

Copyright (c) 2023 Pradeep Kumar Jangid, Namit Gupta, Priyanka Chandani, Shanmugarathinam G.

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.