Skip to main content
SpringerLink
Log in

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

  • Chapter
  • First Online:
Book cover Gully Erosion Studies from India and Surrounding Regions

Part of the book series: Advances in Science, Technology & Innovation ((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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 15:578–600

    Article  Google Scholar 

  • Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth.

    Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Friedman JH. (1991). Multivariate adaptive regression splines. Ann Statist 19(1):1–67.

    Article  Google Scholar 

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Ionita, I., Fullen, M.A., Zgłobicki, W., Poesen, J., 2015. Gully erosion as a natural and human-induced hazard. Nat. Hazards 79, 1–5.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Mararakanye, N., Sumner, P.D., 2017. Gully erosion: a comparison of contributing factors in two catchments in South Africa. Geomorphology 288, 99–110.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Park, N. W. (2010). Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environmental Earth Science, 62(2), 367-376.

    Article  Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Refahi, H., 2009. Soil erosion by water & conservation. Tehran University Press, pp. 10–202 (In Farsi with English Summary).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Selkimäki, M., González-Olabarria, J.R., 2017. Assessing gully erosion occurrence in forestlands in Catalonia (Spain). Land Degrad. Dev. 28 (2), 616–627.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Sidle, R.C., Ochiai, H., 2006. Landslides: Processes, Prediction, and Land Use, Water Res Monograph. vol. 18. American Geophysical Union, Washington, DC, p. 312.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Watershed and Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

    Mahdis Amiri

  2. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

Authors
  1. Mahdis Amiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Reza Pourghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Geography, Raja N. L. Khan Women’s College (Autonomous), Medinipur, West Bengal, India

    Pravat Kumar Shit

  2. Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

    Hamid Reza Pourghasemi

  3. Aarvee Associates Architects, Engineers & Consultants Pvt. Ltd, Hyderabad, India

    Gouri Sankar Bhunia

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Amiri, M., Pourghasemi, H.R. (2020). Mapping and Preparing a Susceptibility Map of Gully Erosion Using the MARS Model. In: Shit, P., Pourghasemi, H., Bhunia, G. (eds) Gully Erosion Studies from India and Surrounding Regions. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-23243-6_27

Download citation

Publish with us

Policies and ethics

Search

Navigation