Gully Erosion Modeling Using GIS-Based Data Mining Techniques in Northern Iran: A Comparison Between Boosted Regression Tree and Multivariate Adaptive Regression Spline

  • Mohsen Zabihi
  • Hamid Reza PourghasemiEmail author
  • Alireza Motevalli
  • Mohamad Ali Zakeri
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 48)


Land degradation occurs in the form of soil erosion in many regions of the world. Among the different type of soil erosion, high sediment yield volume in the watersheds is allocated to gully erosion. So, the purpose of this research is to map the susceptibility of the Valasht Watershed in northern Iran (Mazandaran Province) to gully erosion. For this purpose, spatial distribution of gullies was digitized by sampling and field monitoring. Identified gullies were divided into a training (two-thirds) and validating (one-third) datasets. In the second step, eleven effective factors including topographic (elevation, aspect, slope degree, TWI, plan curvature, and profile curvature), hydrologic (distance from river and drainage density), man-made (land use, distance from roads), and lithology factors were considered for spatial modeling of gully erosion. Then, Boosted Regression Tree (BRT) and Multivariate Adaptive Regression Spline (MARS) algorithms were implemented to model gully erosion susceptibility. Finally, Receiver Operating Characteristic (ROC) used for the assessment of prepared models. Based on the findings, BRT model (AUC = 0.894) had better efficiency than MARS model) AUC = 0.841) for gully erosion modeling and located in very good class of accuracy. In addition, altitude, aspect, slope degree, and land use were selected as the most conditioning agents on the gully erosion occurrence. The results of this research can be used for the prioritization of critical areas and better decision making in the soil and water management in the Valasht Watershed. In addition, the used models are recommended for spatial modeling in other regions of the worlds.


Gully erosion Boosted regression tree Multivariate adaptive regression spline Coupling GIS and R Valasht watershed 


  1. Abeare SM (2009) Comparisons of boosted regression tree, GLM and GAM performance in the standardization of yellowfin tuna catch-rate data from the Gulf of Mexico Longline Fishery. PhD thesis, University of PretoriaGoogle Scholar
  2. Aertsen W, Kint V, Van Orshoven J, Muys B (2011) Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA). Environ Model Softw 26(7):929–937CrossRefGoogle Scholar
  3. Akgun A, Turk N (2011) Mapping erosion susceptibility by a multivariate statistical method: a case study from the Ayvalık region, NW Turkey. Comput Geosci 37:1515–1524CrossRefGoogle Scholar
  4. Barnes N, Luffman I, Nandi A (2016) Gully erosion and freeze-thaw processes in clay-rich soils, northeast Tennessee, USA. Geo Res J 9:67–76Google Scholar
  5. Basofi A, Fariza A, Ahsan AS, Kamal IM (2015) A comparison between natural and Head/tail breaks in LSI (Landslide Susceptibility Index) classification for landslide susceptibility mapping: A case study in Ponorogo, East Java, Indonesia. In: IEEE, 2015 International Conference on Science in Information Technology (ICSITech), Yogyakarta, 27–28 October, pp 337–342Google Scholar
  6. Beguería S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37(3):315–329CrossRefGoogle Scholar
  7. Benjamini Y, Leshno M (2005) Statistical methods for data mining. Data mining and knowledge discovery handbook. Springer, US, pp 565–587Google Scholar
  8. Bergonse R, Reis E (2016) Controlling factors of the size and location of large gully systems: A regression-based exploration using reconstructed pre-erosion topography. CATENA 147:621–631CrossRefGoogle Scholar
  9. Beven KJ, Kirkby MJ, Schofield N, Tagg AF (1984) Testing a physically-based flood forecasting model (TOPMODEL) for three U.K. Catchments. J Hydrol 69:119–143CrossRefGoogle Scholar
  10. Bouchnak H, Felfoul MS, Boussema MR, Snane MH (2009) Slope and rainfall effects on the volume of sediment yield by gully erosion in the Souar lithologic formation (Tunisia). CATENA 78(2):170–177CrossRefGoogle Scholar
  11. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Wadsworth & Brooks, Monterey, CAGoogle Scholar
  12. Chung-Jo F, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30:451–472CrossRefGoogle Scholar
  13. Colkesen I, Sahin EK, Kavzoglu T (2016) Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J Afr Earth Sci 118:53–64CrossRefGoogle Scholar
  14. Conforti M, Aucelli PPC, 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(3):881–898CrossRefGoogle Scholar
  15. Conoscenti C, Agnesi V, Angileri S, Cappadonia C, Rotigliano E, Märker M (2013) A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy. Environ Earth Sci 70(3):1179–1195CrossRefGoogle Scholar
  16. Conoscenti C, Angileri S, Cappadonia C, Rotigliano E, Agnesi V, Märker M (2014) Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology 204:399–411CrossRefGoogle Scholar
  17. Conoscenti C, Ciaccio M, Caraballo-Arias NA, Gómez-Gutiérrez Á, Rotigliano E, Agnesi V (2015) Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Belice River basin (western Sicily, Italy). Geomorphology 242:49–64CrossRefGoogle Scholar
  18. Conoscenti C, Rotigliano E, Cama M, Caraballo-Arias NA, Lombardo L, Agnesi V (2016) Exploring the effect of absence selection on landslide susceptibility models: a case study in Sicily, Italy. Geomorphology 261:222–235CrossRefGoogle Scholar
  19. Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42(3–4):213–228CrossRefGoogle Scholar
  20. Desta L, Adugna B (2012) A field guide on gully prevention and control. Nile Basin Initiative Eastern Nile Subsidiary Action Program (ENSAP), Addis Ababa, Ethiopia, p 67Google Scholar
  21. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, …, Münkemüller T (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1):27–46CrossRefGoogle Scholar
  22. Dotterweich M, Stankoviansky M, Minár J, Koco Š, Papčo P (2013) Human induced soil erosion and gully system development in the Late Holocene and future perspectives on landscape evolution: The Myjava Hill Land, Slovakia. Geomorphology 201:227–245CrossRefGoogle Scholar
  23. Dube F, Nhapi I, Murwira A, Gumindoga W, Goldin J, Mashauri DA (2014) Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District-Zimbabwe. Phys Chem Earth 67:145–152CrossRefGoogle Scholar
  24. Dymond JR, Herzig A, Basher L, Betts HD, Marden M, Phillips CJ, Roygard J (2016) Development of a New Zealand SedNet model for assessment of catchment-wide soil-conservation works. Geomorphology 257:85–93CrossRefGoogle Scholar
  25. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77(4):802–813CrossRefGoogle Scholar
  26. Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189CrossRefGoogle Scholar
  27. Franzluebbers AJ (2010) Principles of Soil Conservation and Management. Vadose Zone J 9(1):199–2001CrossRefGoogle Scholar
  28. Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 1–67CrossRefGoogle Scholar
  29. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 1189–1232Google Scholar
  30. Geissen V, Kampichler C, López-de Llergo-Juárez JJ, Galindo-Acántara A (2007) Superficial and subterranean soil erosion in Tabasco, tropical Mexico: development of a decision tree modeling approach. Geoderma 139:277–287CrossRefGoogle Scholar
  31. Geology Survey of Iran (GSI) (1997) Geology map of the Mazandaran Province.
  32. Golub GH, Heath M, Wahba G (1979) Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21(2):215–223CrossRefGoogle Scholar
  33. Gómez-Gutiérrez Á, Conoscenti C, Angileri SE, 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):291–314CrossRefGoogle Scholar
  34. Goodwin NR, Armston JD, Muir J, Stiller I (2017) Monitoring gully change: A comparison of airborne and terrestrial laser scanning using a case study from Aratula, Queensland. Geomorphology 282:195–208CrossRefGoogle Scholar
  35. Gutiérrez ÁG, Contador FL, Schnabel S (2011) Modeling soil properties at a regional scale using GIS and multivariate adaptive regression Splines. Geomorphometry 2011:53–56Google Scholar
  36. Gutiérrez ÁG, Schnabel S, Contador JFL (2009) Using and comparing two nonparametric methods (CART and MARS) to model the potential distribution of gullies. Ecol Modell 220(24):3630–3637CrossRefGoogle Scholar
  37. Hong H, Pourghasemi HR, Pourtaghi ZS (2016) Landslide susceptibility assessment in Lianhua County (China): A comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118CrossRefGoogle Scholar
  38. Jain SK, Kumar S, Varghese J (2001) Estimation of soil erosion for a Himalayan watershed using GIS technique. Water Resour Manage 15(1):41–54CrossRefGoogle Scholar
  39. Jungerius PD, Matundura J, Van De Ancker JAM (2002) Road construction and gully erosion in West Pokot, Kenya. Earth Surf Proc Land 27(11):1237–1247CrossRefGoogle Scholar
  40. Kuhnert PM, Henderson AK, Bartley R, Herr A (2010) Incorporating uncertainty in gully erosion calculations using the random forests modelling approach. Environmetrics 21:493–509Google Scholar
  41. Le Roux JJ, Sumner PD (2012) Factors controlling gully development: comparing continuous and discontinuous gullies. Land Degrad Dev 23(5):440–449CrossRefGoogle Scholar
  42. Leathwick JR, Elith J, Francis MP, Hastie T, Taylor P (2006) Variation in demersal fish species richness in the oceans surrounding New Zealand: An analysis using boosted regression trees. Mar Ecol Prog Ser 321:267–281CrossRefGoogle Scholar
  43. Li Z, Zhang Y, Zhu Q, Yang S, Li H, Ma H (2017) A gully erosion assessment model for the Chinese Loess Plateau based on changes in gully length and area. CATENA 148:195–203CrossRefGoogle Scholar
  44. Liu J, Sui C, Deng D, Wang J, Feng B, Liu W, Wu C (2016) Representing conditional preference by boosted regression trees for recommendation. Inf Sci 327:1–20CrossRefGoogle Scholar
  45. Luffman IE, Nandi A, Spiegel T (2015) Gully morphology, hillslope erosion, and precipitation characteristics in the Appalachian Valley and Ridge province, southeastern USA. CATENA 133:221–232CrossRefGoogle Scholar
  46. Martinez-Casasnovas JA (2003) A spatial information technology approach for the mapping and quantification of gully erosion. Catena 50(2-4):293–308CrossRefGoogle Scholar
  47. Monsieurs E, Poesen J, Dessie M, Adgo E, Verhoest NE, Deckers J, Nyssen J (2015) Effects of drainage ditches and stone bunds on topographical thresholds for gully head development in North Ethiopia. Geomorphology 234:193–203CrossRefGoogle Scholar
  48. Montgomery D, Dietrich WE (1989) Source areas, drainage density, and channel initiation. Water Resour Res 25(8):1907–1918CrossRefGoogle Scholar
  49. Motevalli A, Vafakhah M (2016) Flood hazard mapping using synthesis hydraulic and geomorphic properties at watershed scale. Stochast Environ Res Risk Assess 30(7):1889–1900CrossRefGoogle Scholar
  50. Mousavi SM, Golkarian A, Naghibi SA, Kalantar B, Pradhan B (2017) GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran. AIMS Geosc 3(1):91–115CrossRefGoogle Scholar
  51. Naghibi SA, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manage 29(14):5217–5236CrossRefGoogle Scholar
  52. Naghibi SA, Pourghasemi HR, Dixon B (2016) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188(1):44CrossRefGoogle Scholar
  53. O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690CrossRefGoogle Scholar
  54. Ollobarren P, Capra A, Gelsomino A, La Spada C (2016) Effects of ephemeral gully erosion on soil degradation in a cultivated area in Sicily (Italy). CATENA 145:334–345CrossRefGoogle Scholar
  55. Osman KT (2014) Soil erosion by water. In: Soil degradation, conservation and remediation. Springer, Netherlands, pp 69–101Google Scholar
  56. Pham BT, Pradhan B, Tien Bui D, Prakash I, Dholakia MB (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India). Environ Model Softw 84:240–250CrossRefGoogle Scholar
  57. Pimentel D (2006) Soil erosion: a food and environmental threat. Environ Dev Sustain 8(1):119–137CrossRefGoogle Scholar
  58. Poesen J, Nachtergaele J, Verstraeten G, Valentin C (2003) Gully erosion and environmental change: importance and research needs. CATENA 50(2):91–133CrossRefGoogle Scholar
  59. Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci 75(3):1–17CrossRefGoogle Scholar
  60. Pourghasemi HR, Mohammady M, Pradhan B (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84CrossRefGoogle Scholar
  61. Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69(1):749–779CrossRefGoogle Scholar
  62. Pourghasemi HR, Rossi M (2016) Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods. Theor Appl Climatol, 1–25Google Scholar
  63. Rahmati O, Haghizadeh A, Pourghasemi HR, Noormohamadi F (2016) Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Nat Hazards 82(2):1231–1258CrossRefGoogle Scholar
  64. Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B (2017) Evaluating the influence of geo-environmental factors on gully erosion in a semi-arid region of Iran: an integrated framework. Sci Total Environ 579:913–927CrossRefGoogle Scholar
  65. Robertson GP, Broome JC, Chornesky EA, Frankenberger JR, Johnson P, Lipson M, …, Thrupp LA (2004) Rethinking the vision for environmental research in US agriculture. Bioscience 54(1):61–65CrossRefGoogle Scholar
  66. Sadeghi SH, Zakeri MA (2015) Partitioning and analyzing temporal variability of wash and bed material loads in a forest watershed in Iran. Earth Syst Sci 124(7):1503–1515CrossRefGoogle Scholar
  67. Sadeghi SHR, Rangavar AS, Bashari M, Abbasi AA (2007) Waterfall erosion as a main factor in ephemeral gully initiation in a part of northeastern Iran. In: 2007 International Symposium on gully erosion: Pamplona, 17–19 September, pp 114–115Google Scholar
  68. Salazar F, Toledo MÁ, Oñate E, Suárez B (2016) Interpretation of dam deformation and leakage with boosted regression trees. Eng Struct 119:230–251CrossRefGoogle Scholar
  69. Schapire RE (2003) The boosting approach to machine learning: an overview. Nonlinear Estimation Classif 171:149–171CrossRefGoogle Scholar
  70. Schonlau M (2005) Boosted regression (boosting): an introductory tutorial and a Stata plugin. Stata 5(3):330–354Google Scholar
  71. Shellberg JG, Spencer J, Brooks AP, Pietsch TJ (2016) Degradation of the Mitchell River fluvial megafan by alluvial gully erosion increased by post-European land use change, Queensland, Australia. Geomorphology 266:105–120CrossRefGoogle Scholar
  72. Shruthi RB, Kerle N, Jetten V, Abdellah L, Machmach I (2015) Quantifying temporal changes in gully erosion areas with object oriented analysis. CATENA 128:262–277CrossRefGoogle Scholar
  73. Stumpf A, Kerle N (2011) Object-oriented mapping of landslides using Random Forests. Remote Sens Environ 115(10):2564–2577CrossRefGoogle Scholar
  74. Superson J, Rodzik J, Reder J (2014) Natural and human influence on loess gully catchment evolution: a case study from Lublin Upland, E Poland. Geomorphology 212:28–40CrossRefGoogle Scholar
  75. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293CrossRefGoogle Scholar
  76. Tebebu TY, Abiy AZ, Zegeye AD, Dahlke HE, Easton ZM, Tilahun SA, …, Steenhuis TS (2010) Surface and subsurface flow effect on permanent gully formation and upland erosion near Lake Tana in the northern highlands of Ethiopia. Hydrol Earth Syst Sci 14(11):2207–2217CrossRefGoogle Scholar
  77. Valentin C, Poesen J, Li Y (2005) Gully erosion: impacts, factors and control. Catena 63(2–3):132–153CrossRefGoogle Scholar
  78. Vanwalleghem T, Bork HR, Poesen J, Schmidtchen G, Dotterweich M, Nachtergaele J, …, De Bie M (2005) Rapid development and infilling of a buried gully under cropland, central Belgium. Catena 63(2):221–243CrossRefGoogle Scholar
  79. Wang LJ, Guo M, Sawada K, Lin J, Zhang J (2015) Landslide susceptibility mapping in Mizunami City, Japan: a comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Catena 135:271–282CrossRefGoogle Scholar
  80. Wantzen KM (2006) Physical pollution: effects of gully erosion on benthic invertebrates in a tropical clear-water stream. Aquat Conserv Mar Freshwater Ecosyst 16(7):733–749CrossRefGoogle Scholar
  81. Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol 79(3):251–266CrossRefGoogle Scholar
  82. Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2015) Erratum to: landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856CrossRefGoogle Scholar
  83. Zabihi M, Pourghasemi HR, Pourtaghi ZS, Behzadfar M (2016) GIS-based multivariate adaptive regression spline and random forest models for groundwater potential mapping in Iran. Environ Earth Sci 75:1–19CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohsen Zabihi
    • 1
  • Hamid Reza Pourghasemi
    • 2
    Email author
  • Alireza Motevalli
    • 1
  • Mohamad Ali Zakeri
    • 1
  1. 1.Department of Watershed Management Engineering, Faculty of Natural ResourcesTarbiat Modares UniversityTehranIran
  2. 2.Department of Natural Resources and Environmental Engineering, College of AgricultureShiraz UniversityShirazIran

Personalised recommendations