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Profile photo of Ronny Berndtsson

Ronny Berndtsson

Professor, Dep Director, MECW Dep Scientific Coordinator

Profile photo of Ronny Berndtsson

Flood susceptibility prediction using MaxEnt and frequency ratio modeling for Kokcha River in Afghanistan

Author

  • Abdul Baser Qasimi
  • Vahid Isazade
  • Ronny Berndtsson

Summary, in English

Flooding is a natural but unavoidable disaster that occurs over time. Flooding threatens human life, property, and resources and affects regional and national economies. Through frequency ratio and MaxEnt modeling, flood sensitivity was determined in the Amu Darya River Basin in Badakhshan Province, Afghanistan. Slope, plan curvature, distance to river, rainfall, aspect, land use, elevation, Normalized Difference Vegetation Index (NDVI), soil type, lithology, Topographic Humidity Index (TWI), and drainage density were used to quantify flood susceptibility. In total, 88 flood points collected from Google Earth were used to train the frequency ratio model to predict flood susceptibility, and 34 GPS-recorded points of the flooded area were used to evaluate the model’s performance. The frequency ratio model displayed a success rate of above 86%. However, using a jackknife entropy test, the MaxEnt model yielded a 97% success rate. The results showed that rainfall, land use, distance to river, and soil type were the most important parameters for evaluating flood sensitivity. The developed models can help planners and decision-makers perform flood susceptibility mapping in the region by determining locations of flooding sensitivity.

Department/s

  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World
  • Division of Water Resources Engineering
  • LTH Profile Area: Water

Publishing year

2023

Language

English

Publication/Series

Natural Hazards

Document type

Journal article

Publisher

Springer

Topic

  • Environmental Sciences

Keywords

  • Afghanistan
  • Flood
  • Frequency ratio
  • Jackknife test
  • Kokcha River
  • MaxEnt model

Status

Epub

ISBN/ISSN/Other

  • ISSN: 0921-030X