Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach.

Autor: Khan NS; Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh., Roy SK; Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. sujitroy.bejoy@gmail.com., Talukdar S; Department of Geography, Asutosh College, University of Calcutta, Kolkata, 700026, India., Billah M; Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh., Iqbal A; Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh., Zzaman RU; Institute of Water and Flood Management, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh., Chowdhury A; Department of Climate and Disaster Management, Jashore University of Science and Technology, Jashore, Bangladesh., Mahtab SB; Department of Water Resources Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh., Mallick J; Department of Civil Engineering, King Khalid University, Abha, Saudi Arabia.
Jazyk: angličtina
Zdroj: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Sep; Vol. 31 (41), pp. 53877-53892. Date of Electronic Publication: 2024 Apr 03.
DOI: 10.1007/s11356-024-33090-7
Abstrakt: Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2's high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July-about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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