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Python Based Modelling of Flood Damage Assessment Using High-Resolution Aerial Imagery

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  • Sumaira Kousar

    (Institute of Geography, University of the Punjab)

Abstract

Flood is a natural disaster that can cause devastating impacts on the community, infrastructure, and the environment. UAVsenable to compute the extent of the flood and to identify the vulnerable areas prone to future flooding, assisting in the formulation of effective mitigation strategies. This study presents a case study of Barwai Khwar, Swat, Khyber Pakhtunkhwa (KPK), pre-flood image attained from Google Earth Pro and the post-flood aerial imagery was collected by using unmanned aerial vehicles (UAVs). To capture the detailed visual information of the flood-affected region and to assess the extent of the flood damage the acquired imagery was then processed by using advanced image processing algorithms to extract essential information, such as inundation extent, floodwater depth,and changes in land cover. This procedure assists in evaluatingthe precise damage assessment and development of effective recovery and mitigation strategies. Results revealed that the 2022 flood in Barwai Khowar'slarge agricultural land was submerged (14758.9 perimeters), leading to a significant loss in crop yield and potential long-term impacts on food security. Additionally, critical infrastructure, including roads, bridges,and buildings suffered substantial damage. The destructed area of the retaining wall is 2184m (2km), housing damage is 1074.9m and 82.6 m of Nullah was calculated in this region. Moreover, the application of such technologies can facilitate more informed and timely responses to natural disasters, enhancing the overall resilience of communities and ecosystems.

Suggested Citation

  • Sumaira Kousar, 2023. "Python Based Modelling of Flood Damage Assessment Using High-Resolution Aerial Imagery," International Journal of Innovations in Science & Technology, 50sea, vol. 5(3), pages 232-252, September.
    • Handle: RePEc:abq:ijist1:v:5:y:2023:i:3:p:232-252
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    References listed on IDEAS

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    2. Maxx Dilley & Robert S. Chen & Uwe Deichmann & Arthur L. Lerner-Lam & Margaret Arnold, 2005. "Natural Disaster Hotspots: A Global Risk Analysis," World Bank Publications - Books, The World Bank Group, number 7376.
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