A hybrid convolutional neural network model coupled with AdaBoost regressor for flood mapping using geotagged flood photographs
Swati Sirsant,
Gilbert Hinge,
Harsh Singh and
Mohamed A. Hamouda ()
Additional contact information
Swati Sirsant: Nirma University
Gilbert Hinge: National Institute of Technology Durgapur
Harsh Singh: University of Engineering & Management
Mohamed A. Hamouda: United Arab Emirates University
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 5, No 27, 5799-5819
Abstract:
Abstract Flood mapping has been crucial to flood hazard identification, mitigation, and preparedness. Development of accurate flood maps in data-scarce regions has always been a challenge. This study presents a multi-stage approach involving topographic feature extraction, CNN-based flood classification, and regression modeling for flood mapping. The study aims to provide accurate estimates of flood depth by leveraging both spatial data and image-based contextual information. The methodology comprises two CNN models followed by a regressor for estimating flood depth using geo-tagged flood photographs. The first CNN model identifies the existence of flood, while the second one identifies the flood severity class (greater than or less than 1 m depth). A modified VGG-16 CNN architecture is employed in the present study for both stages. Finally, the AdaBoost Regressor is employed to estimate precise flood depth using topographical data such as elevation, slope, and topographic position index (TPI) values as the input. The model results showed excellent performance with R2 of 0.93 and RMSE of 25.01% when tested on manually collected flood data for VGP Selva Nagar, a residential area in Chennai, India, for the December 2023 flood. Comparison of the VGG-16 CNN architecture with other standard architectures, such as ResNet50 and InceptionV3, showed the efficacy of the presented model. The presented multi-staged approach, thus, proves to be an effective tool that relies only on geo-tagged flood photographs as the input to develop accurate flood maps. The models developed in this study have significant implications for flood management that can help inform emergency response teams about flood severity and extent, facilitating prompt and effective interventions.
Keywords: Flood inundation; Flood model; Convolutional neural network; AdaBoost regressor (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-07041-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:5:d:10.1007_s11069-024-07041-x
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069
DOI: 10.1007/s11069-024-07041-x
Access Statistics for this article
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk
More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().