Advertisement

Mapping and Preparing a Susceptibility Map of Gully Erosion Using the MARS Model

  • Mahdis Amiri
  • Hamid Reza Pourghasemi
Chapter
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Preparing and mapping gully erosion (GE) is a basic instrumentation to land use projecting and reducing destruction of the land. The purpose of the current investigation was to assess gully erosion spatial modeling using multivariate adaptive regression spline (MARS) model in Maharlou watershed, Fars Province, Iran. The current study is consisted from two important parts including (1) recognizing dependent and variables, e.g., gully erosion inventory map (GEIM) and gully effective agents, and (2) running a famous machine learning algorithm named the MARS in order to gully erosion mapping. Gully erosion inventory map is randomly separated into two categories: training and validation datasets. Then, nine causative factors including land use, distance from rivers, clay percent, geology, pH, NDVI, drainage density, distance from roads, and slope direction are recognized, and their maps are classified in the ArcGIS. Also, the GESM was created using the MARS model in the R statistical environment. The outcomes of the MARS technique of the 30% of the unused gully points used in the modeling procedure based on the ROC curve. Results demonstrated that the ultimate gully erosion map had a top precision with AUC values 96.3% for accuracy data set.

Keywords

Gully erosion MARS GIS and R Fars province 

References

  1. Agnesi, V., Angileri, S., Cappadonia, C., Conoscenti, C., Rotigliano, E., 2011. Multiparametric GIS analysis to assess gully erosion susceptibility: a test in southern Sicily, Italy. Landf. Anal. 7, 15–20.Google Scholar
  2. Al-Abadi, A.M., Al-Ali, A.K., 2018. Susceptibility mapping of gully erosion using GIS-based statistical bivariate models: a case study from Ali Al-Gharbi District, Maysan Governorate, southern Iraq. Environ. Earth Sci. 77 (6), 249.  https://doi.org/10.1007/s12665-018-7434-2.CrossRefGoogle Scholar
  3. Arabameri, A., Pradhan, B., Pourghasemi, H.R., Rezaei, K., Kerle, N., 2018. Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms. Appl. Sci. 8 (8), 1369.  https://doi.org/10.3390/app8081369.CrossRefGoogle Scholar
  4. Azareh, A., Rahmati, O., Rafiei-Sardooi, E., Sankey, J. B., & Lee, S. 2019. Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models. Science of the Total Environment, 655, 684–696.CrossRefGoogle Scholar
  5. Balashi MS, Mcguire AD, Duffy P, Flannigan M, Walsh J, Mellilo J (2009) Assessing the response of area burned to changing climate in western boreal North America using a multivariate adaptive regression splines (MARS) approach. Glob Chang Biol 15:578–600CrossRefGoogle Scholar
  6. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth.Google Scholar
  7. Boardman, J., Favis-Mortlock, D., 1998. Modelling Soil Erosion by Water, first ed. Springer-Verlag Berlin Heidelberg.  https://doi.org/10.1007/978-3-642-58913-3.CrossRefGoogle Scholar
  8. Conoscenti, C., Agnesi, V., Angileri, S., Cappadonia, C., Rotigliano, E., Märker, M., 2013. AGIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy. Environ. Earth Sci. 70 (3), 1179–1195.CrossRefGoogle Scholar
  9. Conoscenti, C., Agnesi, V., Cama, M., Caraballo-Arias, N.A., Rotigliano, E., 2018. Assessment of gully erosion susceptibility using multivariate adaptive regression splines and accounting for terrain connectivity. Land Degrad. Dev. 29 (3), 724–736.CrossRefGoogle Scholar
  10. Conforti, M., Aucelli, P.P.C., Robustelli, G., Scarciglia, F., 2011. Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Nat. Hazards 56, 881–898.CrossRefGoogle Scholar
  11. Dewitte, O., Daoudi, M., Bosco, C., Van Den Eeckhaut, M., 2015. Predicting the susceptibility to gully initiation in data-poor regions. Geomorphology 228, 101–115.CrossRefGoogle Scholar
  12. Dube, F., Nhapi, I., Murwira, A., Gumindoga, W., Goldin, J., Mashauri, D.A., 2014. Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District–Zimbabwe. Phys. Chem. Earth 67, 145–152.  https://doi.org/10.1016/j.pce.2014.02.002 Pt. A/B/C.CrossRefGoogle Scholar
  13. El Maaoui, M.A., Sfar Felfoul, M., Boussema, M.R., Snane, M.H., 2012. Sediment yield from irregularly shaped gullies located on the Fortuna lithologic formation in semi-arid area of Tunisia. Catena 93, 97–104.CrossRefGoogle Scholar
  14. Friedman JH. (1991). Multivariate adaptive regression splines. Ann Statist 19(1):1–67.CrossRefGoogle Scholar
  15. Felicísimo AM, Gómez-Muñoz A (2004) GIS and predictive modelling: a comparison of methods applied to forestal management and decision-making. In: Geographical Information Systems Research UK. Proceedings of the GIS Research UK 12th Annual Conference:143–144. Norwich.Google Scholar
  16. Felicísimo A, Cuartero A, Remondo J, Quirós E (2012) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides. doi: https://doi.org/10.1007/s10346-012-0320.
  17. Gorsevski, P.V., Gessler, P.E., Foltz, R.B., Elliot, W.J., 2006. Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans. GIS 10 (3), 395–415.CrossRefGoogle Scholar
  18. Garosi, Y., Sheklabadi, M., Pourghasemi, H.R., Besalatpour, A.A., Conoscenti, C., Van Oost, K., 2018. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma 330, 65–78.CrossRefGoogle Scholar
  19. Gómez-Gutiérrez, Á., Conoscenti, C., Angileri, S.E., Rotigliano, E., Schnabel, S., 2015. Using topographical attributes to evaluate gully erosion proneness (susceptibility) in two Mediterranean basins: advantages and limitations. Nat. Hazards 79 (1), 291–314.CrossRefGoogle Scholar
  20. Gee, G.W., Bauder, D. 2002. Particle size analysis. In: Dane JH, Topp GC, (eds). Methods of Soil Analysis. Part 4, Physical Methods. Soil Sci. Soc. Am. 5, 255-293.Google Scholar
  21. Hong, H., Pourghasemi, H.R., Pourtaghi, Z.S. 2016. Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical. Geomorphology. 259, 105-118.CrossRefGoogle Scholar
  22. Ionita, I., Fullen, M.A., Zgłobicki, W., Poesen, J., 2015. Gully erosion as a natural and human-induced hazard. Nat. Hazards 79, 1–5.CrossRefGoogle Scholar
  23. Kennison RF, Cox J (2013) Health and functional limitations predict depression scores in the health and retirement study; results straight from MARS. Calif J Health Promot 11(1):97–108.CrossRefGoogle Scholar
  24. Lesschen, J.P., Kok, K., Verburg, P.H., Cammeraat, L.H., 2007. Identification of Vulnerable Areas for Gully Erosion under Different Scenarios of Land Abandonment in Southeast Spain. Catena 71 (1), 110–121.  https://doi.org/10.1016/j.catena.2006.05.014.CrossRefGoogle Scholar
  25. Mclean, E.O. (1982) Soil pH and Lime Requirement. In: Page, A.L., Ed., Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties, American Society of Agronomy, Soil Science Society of America, (pp. 199-224).Google Scholar
  26. Mararakanye, N., Sumner, P.D., 2017. Gully erosion: a comparison of contributing factors in two catchments in South Africa. Geomorphology 288, 99–110.CrossRefGoogle Scholar
  27. Märker, M., Pelacani, S., Schröder, B., 2011. A functional entity approach to predict soil erosion processes in a small Plio-Pleistocene Mediterranean catchment in Northern Chianti, Italy. Geomorphology 125, 530–540.  https://doi.org/10.1016/j.geomorph.2010.10.022.CrossRefGoogle Scholar
  28. Meliho, M., Khattabi, A., Mhammdi, N., 2018. A GIS-based approach for gully erosion susceptibility modelling using bivariate statistics methods in the Ourika watershed, Morocco. Environ. Earth Sci. 77 (18), 655.  https://doi.org/10.1007/s12665-018-7844-1.CrossRefGoogle Scholar
  29. Milborrow S (2009) Derived from mda: mars by Trevor Hastie and RobTibshirani. earth: Multivariate Adaptive Regression Splines, 2009.R Package, http://CRAN.R-project.org/package=earth.
  30. Park, N. W. (2010). Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environmental Earth Science, 62(2), 367-376.CrossRefGoogle Scholar
  31. Poesen, J., Vanwalleghem, T., Deckers, J., 2018. Gullies and closed depressions in the Loess Belt: scars of human–environment interactions. Landscapes and Landforms of Belgium and Luxembourg. Springer, Cham, pp. 253–267.CrossRefGoogle Scholar
  32. Pourghasemi, H. R., Moradi, H. R., Fatemi Aghda, S. M., Gokceoglu, C., Pradhan, B. 2014. GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran), Arabian Journal of Geosciences. 7, 1857-1878.CrossRefGoogle Scholar
  33. Pourghasemi, H.R., Gayen, A., Panahi, M., Rezaie, F., Blaschke, T., 2019. Multi-hazard probability assessment and mapping in Iran. Science of the Total Environment, 692, 556–571.CrossRefGoogle Scholar
  34. Pourghasemi, H.R., Yousefi, S., Kornejady, A., Cerdà, A., 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci. Total Environ. 609, 764–775.CrossRefGoogle Scholar
  35. Rahmati, O., Pourghasemi, H.R., 2017. Identification of critical flood prone areas in data scarce and ungauged regions: A comparison of three data mining models. Water Resour. Manag. 31 (5), 1473–1487.CrossRefGoogle Scholar
  36. Rahmati, O., Haghizadeh, A., Pourghasemi, H.R., Noormohamadi, F., 2016. Gully erosion susceptibility mapping: the role of GIS based bivariate statistical models and their comparison. Nat. Hazards 82 (2), 1231–1258.  https://doi.org/10.1007/s11069-016-2239-7.CrossRefGoogle Scholar
  37. Refahi, H., 2009. Soil erosion by water & conservation. Tehran University Press, pp. 10–202 (In Farsi with English Summary).Google Scholar
  38. Sankey, J.B., Draut, A.E., 2014. Gully annealing by aeolian sediment: field and remote sensing investigation of aeolian–hillslope–fluvial interactions, Colorado River corridor, Arizona, USA. Geomorphology 220, 68–80.CrossRefGoogle Scholar
  39. Selkimäki, M., González-Olabarria, J.R., 2017. Assessing gully erosion occurrence in forestlands in Catalonia (Spain). Land Degrad. Dev. 28 (2), 616–627.CrossRefGoogle Scholar
  40. Setianto, A., & Triandini, T. (2013). Comparison of Kriging and Inverse distance Weighted (IDW) Interpolation methods in Lineament extraction and Analysis. Journal of Southeast Asian Applied Geology. 5(1), 21-29.Google Scholar
  41. Sezer, E.A., Pradhan, B., Gokceoglu, C., 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst. Appl. 38, 8208–8219.CrossRefGoogle Scholar
  42. Swarnkar, S., Malini, A., Tripathi, S., Sinha, R., 2018. Assessment of uncertainties in soil erosion and sediment yield estimates at ungauged basins: an application to the Garra River basin, India. Hydrol. Earth Syst. Sci. 22, 2471–2485.  https://doi.org/10.5194/hess-22-2471-2018.CrossRefGoogle Scholar
  43. Shit, P.K., Paira, R., Bhunia, G., Maiti, R., 2015. Modeling of potential gully erosion hazard using geo-spatial technology at Garbheta block, West Bengal in India. Model. Earth Syst. Environ. 1 (1–2), 1–16.  https://doi.org/10.1007/s40808-015-0001-x.
  44. Sidle, R.C., Ochiai, H., 2006. Landslides: Processes, Prediction, and Land Use, Water Res Monograph. vol. 18. American Geophysical Union, Washington, DC, p. 312.CrossRefGoogle Scholar
  45. Sigaroodi, S. K., Chen, Q., Ebrahimi, S., Nazari, A., & Choobin, B. 2014. Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran. Hydrology. Earth System. Sciences, 18, 1-12.CrossRefGoogle Scholar
  46. United States Department of Agriculture, Soil Conservation Service (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.Google Scholar
  47. Umar, Z., Pradhan, B., Ahmad, A., Jebur, M.N., Tehrany, M.S., 2014. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 118, 124–135.CrossRefGoogle Scholar
  48. Wang, L., Wei, S., Horton, R., Shao, M.A., 2011. Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Plateau of China. Catena 87(1), 90–100.CrossRefGoogle Scholar
  49. Yesilnacar, E. K. (2005). The application of computational intelligence to landslide susceptibility mapping in Turkey (Ph.D Thesis Department of Geomatics the University of Melbourne).Google Scholar
  50. Zakerinejad, R., Maerker, M., 2014. Prediction of gully erosion susceptibilities using detailed terrain analysis and maximum entropy modeling: a case study in the Mazayejan Plain, Southwest Iran. Geogr. Fis. Din. Quaternaria 37 (1), 67–76.  https://doi.org/10.4461/GFDQ.2014.37.7.CrossRefGoogle Scholar
  51. Zakerinejad, R., Maerker, M., 2015. An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayejan basin, southwestern Iran. Nat. Hazards 79 (1), 25–50.CrossRefGoogle Scholar
  52. Zabihi, M., Mirchooli, F., Motevalli, A., Darvishan, A.K., Pourghasemi, H.R., Zakeri, M. A., Sadighi, F., 2018. Spatial modelling of gully erosion in Mazandaran Province, northern Iran. Catena 161, 1–13.CrossRefGoogle Scholar
  53. Zabihi, M., Pourghasemi, H.R., Pourtaghi, Z., & Behzadfar, M. 2016. GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environment Earth Science, 75, 1-19.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mahdis Amiri
    • 1
  • Hamid Reza Pourghasemi
    • 2
  1. 1.Department of Watershed and Arid Zone ManagementGorgan University of Agricultural Sciences and Natural ResourcesGorganIran
  2. 2.Department of Natural Resources and Environmental Engineering, College of AgricultureShiraz UniversityShirazIran

Personalised recommendations