Abstract
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.
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The datasets generated during and/or analysed in the current study can be available from the corresponding author on reasonable request.
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Authors thankfully acknowledge the Deanship of Scientific Research for proving administrative and financial supports.
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Authors thankfully acknowledge the Deanship of Scientific Research for proving administrative and financial supports. Funding for this research was given under award numbers RGP2/442/44 by the Deanship of Scientific Research; King Khalid University, Ministry of Education, Kingdom of Saudi Arabia.
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Nafis Sadik Khan and Sujit Kumar Roy: conceptualization; data curation; formal analysis; investigation; methodology; project administration; resources; software; supervision; validation; visualization; roles/writing—original draft; writing—review and editing. Swapan Talukdar: conceptualization; methodology; visualization; roles/writing—original draft; writing—review and editing, data wrangling. Md. Mostaim Billah, Ashik Iqbal and Rashed Uz Zzaman: Resources; supervision; investigation, validation; visualization, writing—review and editing. Md. Arif Chowdhury: roles/writing—original draft. Sania B Mahtab and Javed Mallick: investigation, supervision, writing—review ang editing. All authors read and approved the final manuscript.
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Khan, N.S., Roy, S.K., Talukdar, S. et al. Empowering real-time flood impact assessment through the integration of machine learning and Google Earth Engine: a comprehensive approach. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33090-7
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DOI: https://doi.org/10.1007/s11356-024-33090-7