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Spatial prediction of susceptibility to gully erosion in Jainti River basin, Eastern India: a comparison of information value and logistic regression models

  • Tusar kanti Hembram
  • Gopal Chandra Paul
  • Sunil Saha
Original Article
  • 12 Downloads

Abstract

The research on effective methods for assessing areas susceptible to gully erosion has environmental, agricultural and economic benefits to society. This study aimed to identify the areas with high susceptibility to gully erosion using two statistical methods and compare the performances. Remote sensing and field data along with two statistical methods, information value (InfVal) and logistic regression, were applied in the analysis. Gullies, boundary demarcation and regulating erosion processes were surveyed and mapped in a GIS environment. Gully sites were split into training and validation set for modelling and validating susceptibility results. The geo-environmental variables selected, according to study area, were land use/land cover, slope steepness, slope aspect, elevation range, length-slope factor, topographical wetness and position index, stream power index, geology, soil type, rainfall erosivity index and altitude. Weights for each class of each variable were computed through InfVal method and probabilities of occurrence of 1,000,000 random locations were extracted to run the logistic regression operation. A weighted linear combination approach and an inverse distance weighted approach were applied for the InfVal and logistic regression methods, respectively, to generate the gully proneness maps. Both results were classified into five susceptibility classes and they depict 18.44% (Infval) and 12.67% (logistic regression) area of the catchment as high to very highly susceptible to gully erosion. Correlation analysis between the results revealed a coefficient value of 0.8379. For InfVal model, the success and prediction rate is 78% and 75% whereas for the logistic regression model, it is 73% and 72.9% accordingly.

Keywords

Gully erosion Information value Logistic regression Susceptibility mapping Success and prediction rate curve. 

Notes

Acknowledgements

The authors would like to express cordial thanks to our respected teachers of Department of Geography, University of Gour Banga, who have always been mentally, economically and infrastructurally supported ourselves. Authors would also like to thanks the inhabitants of this basin because they have helped a lot during our field visit. At last, authors would like to acknowledge all of the agencies and individuals specially, Survey of India (SOI), Geological Survey of India (GSI) and USGS for obtaining the maps and data required for the study.

Supplementary material

40808_2018_560_MOESM1_ESM.docx (14 kb)
Supplementary material 1 (DOCX 13 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tusar kanti Hembram
    • 1
  • Gopal Chandra Paul
    • 1
  • Sunil Saha
    • 1
  1. 1.Department of GeographyUniversity of Gour BangaMaldaIndia

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