Enhancing flood susceptibility modeling using integration of multi-source satellite imagery and multi-input convolutional neural network

Shadi Maddah (), Barat Mojaradi () and Hosein Alizadeh ()
Additional contact information
Shadi Maddah: Iran University of Science and Technology
Barat Mojaradi: Iran University of Science and Technology
Hosein Alizadeh: Iran University of Science and Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 3, No 16, 2824 pages

Abstract: Abstract Flood susceptibility maps are vital tools for implementing prevention and mitigation strategies. This study describes the potential application of convolutional neural networks (CNN) from two standpoints, single-input and multi-input CNN, to improve flood susceptibility modeling. Firstly, optical (Sentinel-2 and Landsat-8) and radar (Sentinel-1) satellite images were integrated to identify flooded and non-flooded areas. Moreover, a geospatial database with thirteen geo-environmental features including altitude, slope, rainfall, land use and land cover (LULC), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), aspect, curvature, drainage density, topographic wetness index (TWI), stream power index (SPI), soil texture, and distance from the river, was created in Aqqala County, Golestan Province, Iran. This study concentrates on improving the prediction performance by enhancing the feature extraction capabilities of the CNN model. To achieve this, a multi-input CNN model is developed and compared with the single-input CNN model. The validation results in terms of the area under the receiver operating characteristic (ROC) curve (AUC) showed that the multi-input CNN model in training (AUC = 0.998) and testing (AUC = 0.946) performed better than the single-input CNN model in training (AUC = 0.987) and testing (AUC = 0.896). The results also demonstrated the potential of the multi-input CNN model as a promising flood susceptibility prediction model.

Keywords: Flood susceptibility mapping; Sentinel-1 SAR; Optical images; Single-input CNN; Multi-input CNN; Deep learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06764-1 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:3:d:10.1007_s11069-024-06764-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-06764-1

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 ().