Document Type : Original Research Paper

Authors

1 TMU

2 geology department

3 geomorphology department, tehran university

Abstract

at first, a gully erosion inventory map was prepared using extensive field surveys and interpretation of aerial photographs, of which 172 gully erosion, 70% (121 gully) are used for modeling and 30% (51 gully) are used for validation purposes. In the next step, for the selection of parameters, after their initial identification, the multicollinearity analysis test was performed using coefficients of tolerance and variance inflation factor and the parameters with multicollinearity were deleted due to the reduced accuracy of the modeling, , Finally, 12 parameters were selected for modeling. The results of determining the significance of the criteria by entropy index method showed that elevation, lithology and NDVI parameters had the greatest effect on the occurrence of gully. In order to validate the model, the prediction rate and success rate as well as the SCAI index were used. The validation results showed that the combined model with a prediction rate of 956.0 (95.6%) and a success rate of 92.33 (92.3%) had excellent predictive accuracy and compared with the entropy index and evidential belief function with prediction rates of 0.932 and 0.917, and the success rates of 0.911 and 0.901, have higher accuracy. According to the results of the SCAI, class differentiation was appropriate in the combined model. According to the results, 28.95 percentage of the study area is located in high and very high susceptibility classes. The results of this research can be used by landuse planners to expansion development activities.

Keywords

Main Subjects

References
Arabameri, A. and Pourghasemi, H. R., 2019- Spatial Modeling of Gully Erosion Using Linear and Quadratic Discriminant Analyses in GIS and R. Edit; Pourghasemi, H. R., Gokceoglu, C. Spatial Modeling in GIS and R for Earth and Environmental Sciences. First edition. Elsevier publication. p.796.
Arabameri, A., Pourghasemi, H. R. and Cerda, A., 2017a- Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Science of the Total Environment, V. 613–614, p. 1385- 1400.
Arabameri, A., Pourghasemi, H. R. and Yamani, M., 2017b- Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environmental Earth Sciences, V. 76, p. 832.
Arabameri, A., Pradhan, B. and Rezaei, K., 2019a- Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. Journal of environmental management, V. 232, p. 928- 942.
Arabameri, A., Pradhan, B. and Rezaei, K., 2019b- Spatial prediction of gully erosion using ALOS PALSAR data and ensemble bivariate and data mining models. Geosciences Journal, p.1- 18
Arabameri, A., Pradhan, B., Pourghasemi, H. R. and Rezaei, K., 2018a- Identification of erosion-prone areas using different multi-criteria decision-making techniques and GIS. Geomatics, Natural Hazards and Risk, V. 9, p. 1129- 1155.
Arabameri, A., Pradhan, B., Pourghasemi, H. R., Rezaei, K. and Kerle, N., 2018b- Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms. Applied sciences, V. 8, p. 1369.
Arabameri, A., Pradhan, B., Rezaei , K., Yamani, M., Pourghasemi, H. R.  and Lombardo, L., 2018c- Spatial modelling of gully erosion using Evidential Belief Function, Logistic Regression and a new ensemble EBF–LR algorithm. Land Degradation and Development, V. 29, p. 4035- 4049.
Arabameri, A., Rezaei, K., Pourghasemi, H. R., Lee, S. and Yamani, M., 2018d- GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique. Environmental Earth Sciences, V. 77, p. 628.
Conforti, M., Aucelli, P. P., Robustelli, G. and Scarciglia, F., 2011- Geomorphology and GIS analysis formapping gully erosion susceptibility in the Turbolo streamcatchment (Northern Calabria, Italy). Natural Hazards, V. 56, p. 881- 898.
Chaplot, V., 2013- Impact of terrain attributes, parent material and soil types on gully erosion. Geomorphology, V. 186, p. 1- 11.
Conoscenti, C., Angileri, S., Cappadonia, C., Rotigliano, E., Agnesi, V. and Ma¨rker, M., 2014- Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology, V. 204, p. 399- 411.
De ploey, J., 1989- A model for headcut retreat in rills and gullies. Catena, V. 14, p. 81- 86.
El Maaoui, M. A., Sfar Felfoul, M., Boussema, M. R. and Snane, M. H., 2012- Sediment yield from irregularly shaped gullies located on the Fortuna lithologic formation in semi-arid area of Tunisia. Catena, V. 93, p.  97- 104.
Ghorbani Nejad, S., Falah, F., Daneshfar, M., Haghizadeh, A. and Rahmati, O., 2016- Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto International, V. 32, p. 167- 187.
Go´mez-Gutie´rrez, A., Conoscenti, C., Angileri, S. E., Rotigliano, E. and  Schnabel, S., 2015- Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two mediterranean basins: advantages and limitations. Nat Hazards, V. 79, p. 291- 314.
Golestani, G., Issazadeh, L. and Serajamani, R., 2014- Lithology effects on gully erosion in Ghoori chay  tershed using RS and GIS. Int J Biosci, V. 4, p. 71- 76.
Kornejady, A., Heidari, K. and Nakhavali, M., 2015- Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam. Iran. Environmental Resources Research, V. 3, p. 85- 109.
Lee, S., Hwang, J. and Park, I., 2013- Application of data-driven evidential belief functions to landslide susceptibility mapping in Jinbu, Korea, Catena, V. 100, p. 15- 30.
Lo, C. P. and Yeung, A. K. W., 2002- Concepts and Techniques of Geographic Information System. New Jersey: Pearson Education Inc.
Pourghasemi, H. R. and  Kerle, N., 2016- Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environal Earth Sciences, V. 75, p.185.
Pourghasemi, H. R., Yousefi, S., Kornejady, A. and Cerdà, A., 2017- Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of the Total Environment, V. 609, p. 764- 775.
Rahmati, O., Haghizadeh. A., Pourghasemi, H. R. and Noormohamadi, F., 2016- Gully erosion susceptibility mapping: the role of GIS based bivariate statistical models and their comparison. Nat Hazards, V. 82, p.1231- 1258.
Rahmati, O., Nazari Samani, A., Mahdavi, M., Pourghasemi, H. R. and Zeinivand, H., 2014- Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arabian Journal of Geosciences, V. 8, p. 7059- 7071.
Rahmati, O., Tahmasebipour, N., Haghizadeh, A., Pourghasemi, H. R. and Feizizadeh, B., 2017- Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: An integrated framework. Science of the Total Environment, V. 579, p. 913- 927.
Regmi, N. R., Giardino, J. R. and Vitek, J. D., 2010- Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, V. 115, p. 172- 187.
Shafer, G., 1976- A mathematical theory of evidence, vol 1. Princeton University, Princeton.
Shrestha, S., Kang, T. S. and Suwal, M. K., 2017- An Ensemble Model for Co-Seismic Landslide Susceptibility Using GIS and Random Forest Method. ISPRS Int. J. Geo-Inf,  V. 6, p. 365.
Süzen, M. L. and Doyuran, V., 2004- A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environmental Geology, V. 45, p. 665- 679.
Tahmassebipoor, N., Rahmati, O., Noormohamadi, F. and Lee, S., 2016- Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arabian Journal of Geosciences, V. 9, p. 79.
Torri, D., Poesen, J., Borselli, L., Bryan, R. and Rossi, M., 2012- Spatial variation of bed roughness in eroding rills and gullies. Catena, V. 90, p. 76- 86.
USDA-SCS, 1966- Procedure for determining rates of land damage, land depreciation, and volume of sediment produced by gully erosion. Technical Release No. 32. US GPO 1990-261-419:20727/SCS.US Government Printing Office, Washington, DC.
Youssef, A., Pradhan, B., Jebur, M. N. and El-Harbi, H., 2017- Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environmental Earth Sciences, V. 73, p. 3745- 3761.