Publication:
Mapping and analysing framework for extreme precipitation‑induced fooding
Mapping and analysing framework for extreme precipitation‑induced fooding
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Date
2023
Authors
Vikas Kumar Rana, Nguyen Thi Thuy Linh, Pakorn Ditthakit, Ismail Elkhrachy, Trinh Trong Nguyen, Nguyet Minh Nguyen.
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Abstract
A conceptual framework is proposed, to identify food afected locations that should be considered in order to lessen the consequences of naturally occurring disaster. Sentinel-1 data are used to evaluate the performance of automatic Otsu’s method and machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine (SVM), CART, Minimum Distance (MD), K-nearest neighbour (KNN) and KD Tree KNN (KD-KNN)) to characterise fooded region. The study provided a holistic spatial assessment of food inundation in the region due to impact of the extreme precipitation. The most adequate performance based on compound value is achieved by KNN (Cv = 2) followed by SVM (Cv = 2.25) ML model and Otsu’s thresholding method (Cv = 2.5). The validation site results reveal that Vertical transmit and Vertical received (VV) polariza tion performs signifcantly better than Vertical transmit and Horizontal received (VH) polarization. The most accurate food extent produced by Otsu’s thresholding method (overall accuracy of 94.98%) and MD (overall accuracy of 88.98%) are used to evaluate the indicative number of individuals and buildings at risk within the study areas using Gridded Population of the World Version 4 (GPWv4), Global ML Building Footprints by Microsoft and OpenStreetMap building data
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Flood,
Google Earth Engine,
Machine Learning,
OpenStreetMap,
Synthetic Aperture Rada