Journal of Range and Watershed Managment

Evaluating the Different Statistical Models for Flood Susceptibility Mapping in Guilan Province

Document Type : Research Paper

Authors

Eisa Gholami 1
Mehdi Vatakhah 2
Seyed Jalil Alavi 3

1 Department of Watershed management, Faculty of Natural Resources, Tarbiat Modares University, Noor

2 2Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University

3 Tarbiat Modares University Faculty of Natural Resources

https://doi.org/10.22059/jrwm.2020.270045.1320
Abstract
Due to the lack of information in most of the watersheds, many researchers attempt to use spatial analysis within Geographic Information System (GIS) in hydrological and Flood Prone (FP) area studies. The present study was designed to compare the efficiency of three models i.e. Support Vector Machine (SVM), Generalized Linear Model (GLM) and Generalized Additive Model (GAM) for preparing the flood susceptibility mapping in Guilan province, Iran.
For this purpose, slope, aspect, plan curvature, elevation, distance from the river, drainage density, geology, land use, Topographic Wetness Index (TWI) and Stream Power Index (SPI) layers were derived in GIS (ArcGIS and SAGA-GIS). Using 220 flood locations, 70% and 30% out of total flood locations were then used to calibrate and to validate the performance of the models, respectively. The evaluation results of the models accuracy using the area under the curve (AUC) and Kappa indices showed that in terms of AUC, the SVM with 0.835 and the GAM with 0.827, and the GLM with of 0.79 performed very good and good classes, respectively. In terms of Kappa index, the SVM with 0.58, GAM with 0.53 and GLM with 0.48 are performed good and acceptable classes, respectively. Therefore, based on the mentioned indices, the SVM superior to other two models for identifying the flood susceptibility areas.

Keywords

Flood Inundation Area
Data Mining
Data Driven Models
Modelling
Receiving Operating Curve (ROC)
Guilan Province
Journal of Range and Watershed Managment
Volume 72, Issue 4
Winter 2020
Pages 1011-1022

  • Receive Date 22 November 2018
  • Revise Date 19 March 2020
  • Accept Date 27 January 2020
  • Publish Date 20 February 2020

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