Advancing flood disaster management: leveraging deep learning and remote sensing technologies

• Autorzy: Roohi Mohammad , Reza Ghafouri Hamid , Mohammad Ashrafi Seyed

Identyfikatory

DOI

10.1007/s11600-024-01481-6

• Języki publikacji: EN

Abstrakty

EN

Floods are among the most widespread and devastating natural disasters, accounting for 47% of all weather-related events and affecting over 2.3 billion people, particularly in Asia. Assessing flood-prone areas is crucial for effective disaster risk reduction, but existing flood damage estimation methods, such as depth-damage functions, often lack regional adaptability and accuracy. This study addresses this gap by integrating geospatial data, remote sensing, and artificial intelligence (AI) to identify flood-affected areas in the Kan basin, Tehran. We applied deep learning methods, specifically U-Net and fully convolutional neural network (FCN) algorithms, to optical and radar images from four flood events. Our results demonstrate that the U-Net model achieves significantly higher accuracy (88%) in identifying flood-affected areas compared to the FCN model (55% accuracy). This superior performance is further supported by the mean intersection over union (mIoU) values, with U-Net achieving 0.65, compared to 0.55 for FCN. The key message of this investigation is that deep learning, particularly the U-Net model, applied to remote sensing data holds significant promise for enhancing flood monitoring, early warning systems, and disaster management strategies by enabling more accurate and timely flood assessments.

Słowa kluczowe

EN

flood damage assessment; remote sensing; deep learning; U-Net; FCN

PL

ocena szkód powodziowych; teledetekcja; uczenie głębokie; U-Net

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2025
• Tom: Vol. 73, no. 1
• Strony: 557--575
• Opis fizyczny: Bibliogr. 64 poz.

Twórcy

autor

Roohi Mohammad

• Department of Civil Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

• https://orcid.org/0009-0000-7934-0451

autor

Reza Ghafouri Hamid

• Department of Civil Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

autor

Mohammad Ashrafi Seyed

• Department of Civil Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

• https://orcid.org/0000-0001-7884-9029

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).