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Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur)
Version 1
: Received: 24 December 2020 / Approved: 25 December 2020 / Online: 25 December 2020 (10:36:39 CET)
How to cite:
Naemitabar, M.; Zangeneh Asadi, M.A.; Amirahmadi, A.; Goli Mokhtari, L. Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur). Preprints2020, 2020120650. https://doi.org/10.20944/preprints202012.0650.v1
Naemitabar, M.; Zangeneh Asadi, M.A.; Amirahmadi, A.; Goli Mokhtari, L. Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur). Preprints 2020, 2020120650. https://doi.org/10.20944/preprints202012.0650.v1
Naemitabar, M.; Zangeneh Asadi, M.A.; Amirahmadi, A.; Goli Mokhtari, L. Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur). Preprints2020, 2020120650. https://doi.org/10.20944/preprints202012.0650.v1
APA Style
Naemitabar, M., Zangeneh Asadi, M.A., Amirahmadi, A., & Goli Mokhtari, L. (2020). Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur). Preprints. https://doi.org/10.20944/preprints202012.0650.v1
Chicago/Turabian Style
Naemitabar, M., Abolghasem Amirahmadi and Leila Goli Mokhtari. 2020 "Evaluating and Zoning Flood Susceptibility Using Curve Number (CN) Logistic and Hydrological Regression Model (Case Study of Kalateh Qanbar Drainage Basin, Nishabur)" Preprints. https://doi.org/10.20944/preprints202012.0650.v1
Abstract
Spatial evaluation of flood-prone areas at the drainage basins is one of the basic strategies in the field of flood risk management. The present study aims to investigate the efficiency of the CN logistic and hydrological regression model for predicting and zoning floods. In the first stage, 13 runoff parameters, hydrologic soil groups (HSGs), slope, lithology, drainage density (DD), land curvature, elevation, distance to waterways/rivers, topographic wetness index (TWI), stream power index (SPI), rainfall, land use, and NDVI were employed. In the SCS-CN model of the drainage basin, the infiltration rate (S) and runoff amount (Q) were determined. The weights of the used layers were weighted by the AHP. Also, a flood zoning map of the drainage basin with different 5, 15, 25, and 50 year return periods was drawn by applying the weights of the layers. To ensure the accuracy of the zoning map with the logistic regression model, the ROC curve, and the area below the curve were used. The results showed that for the prediction rate, the AUC is 0.81%, indicating that the model has acceptable accuracy. The most important factors affecting flood are geological index; distance to waterways/rivers; and NDVI in the logistic regression model, and slope, DD, rainfall, and land use in the SCS-CN model respectively. 30 to 46% of the drainage basin area during 5 to 50 year periods has moderate flood potential, and 28 to 34% has high potential.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
