Gully erosion mapping based on hydro-geomorphometric factors and geographic information system

Environ Monit Assess. 2023 May 25;195(6):721. doi: 10.1007/s10661-023-11197-7.


Delineation of areas susceptible to gully erosion with high accuracy and low cost using significant factors and statistical model is essential. In the present study, a gully susceptibility erosion map (GEM) was developed using hydro-geomorphometric parameters and geographic information system in western Iran. For this aim, a geographically weighted regression (GWR) model was applied, and its results compared to frequency ratio (FreqR) and logistic regression (LogR) models. Almost twenty effective parameters on gully erosion were detected and mapped in the ArcGIS®10.7 environment. These layers and gully inventory maps (375 gully locations) were prepared using aerial photographs, Google Earth images, and field surveys divided into 70% and 30% (263 and 112 samples) ArcGIS®10.7. The GWR, FreqR, and LogR models were developed to generate gully erosion susceptibility maps. The area under the receiver/relative operating characteristic curve (AUC-ROC) was calculated to validate the generated maps. Based on the LogR model results, soil type (SOT), rock unit (RUN), slope aspect (SLA), Altitude (ALT), annual average precipitation (AAP), morphometric position index (MPI), terrain surface convexity (TSC), and land use (LLC) factors were the most critical conditioning parameters, respectively. The AUC-ROC results show the accuracy of 84.5%, 79.1%, and 78% for GWR, LogR, and FreqR models, respectively. The results show high performance for the GWR compared to LogR and FreqR multivariate and bivariate statistic models. The application of hydro-geomorphological parameters has a significant role in the gully erosion susceptibility zonation. The suggested algorithm can be used for natural hazards and human-made disasters such a gully erosion on a regional scale.

Keywords: Frequency ratio; Geographically weighted regression; Gully erosion; Logistic regression.

MeSH terms

  • Algorithms
  • Environmental Monitoring*
  • Geographic Information Systems*
  • Humans
  • Logistic Models
  • Models, Statistical