Advertisement

Application of Fuzzy Analytical Network Process Model for Analyzing the Gully Erosion Susceptibility

  • Bahram Choubin
  • Omid RahmatiEmail author
  • Naser Tahmasebipour
  • Bakhtiar Feizizadeh
  • Hamid Reza Pourghasemi
Chapter
Part of the Advances in Natural and Technological Hazards Research book series (NTHR, volume 48)

Abstract

Soil erosion is one of the most important processes in land degradation especially in semi-arid areas such as Iran. Awareness from susceptible areas to erosion is essential for decreasing the damages and restoration of the eroded areas and achieving the sustainable development goals. Thus, the main purposes of this study are prioritizing the effective variables in engender and extend of gully erosion and predicting the gully erosion susceptibility map in the Kashkan-Poldokhtar Basin, Iran. In order to achieve this purpose, the fuzzy analytical network process (Fuzzy ANP) was applied by means of considering the interrelationship network within the effective criteria on the gully erosion. The assessing step were conducted by the fuzzy approach in associate with the expert’s opinions for determining the susceptible areas to gully erosion. Eventually, gully erosion susceptibility map was produced based on Fuzzy ANP weights and GIS aggregation functions. Results were validated by applying the known gullies collected in field surveys by GPS. The ROC curve was applied to investigate the susceptibility model’s performance. Results of the Fuzzy-ANP was revealed that drainage density, soil texture, and lithology are most important factors for gully erosion. In addition, results delivered the accuracy of 90.4% for the study area which is very acceptable. This research highlights that Fuzzy ANP as an efficient approach for producing the susceptibility map of gully erosion, especially in an environment with incomplete datasets.

Keywords

Gully erosion Susceptibility Fuzzy ANP GIS Iran 

Notes

Acknowledgements

We thank the Editors, Dr. Pourghasemi and Dr. Rossi, and two anonymous reviewers for their suggestions and comments.

References

  1. Angileri SE, Conoscenti C, Hochschild V, Märker M, Rotigliano E, Agnesi V (2016) Water erosion susceptibility mapping by applying Stochastic gradient treeboost to the imera Meridionale River Basin (Sicily, Italy). Geomorphology 262:61–76CrossRefGoogle Scholar
  2. Chang KL, Liao SK, Tseng TW, Liao CY (2015) An ANP based TOPSIS approach for Taiwanese service apartment location selection. Asia Pacific Manag Rev 20(2):49–55CrossRefGoogle Scholar
  3. Chaplot V, Coadou le Brozec E, Silvera N, Valentin C (2005) Spatial and temporal assessment of linear erosion in catchments under sloping lands of northern Laos. CATENA 63:167–184CrossRefGoogle Scholar
  4. Chen Z, Chen W, Li C, Pu Y, Sun H (2016) Effects of polyacrylamide on soil erosion and nutrient losses from substrate material in steep rocky slope stabilization projects. Sci Total Environ 554:26–33CrossRefGoogle Scholar
  5. Choubin B, Darabi H, Rahmati O, Sajedi-Hosseini F, Kløve B (2018) River suspended sediment modelling using the CART model: a comparative study of machine learning techniques. Sci Total Environ 615:272–281CrossRefGoogle Scholar
  6. Choubin B, Solaimani K, Roshan MH, Malekian A (2017) Watershed classification by remote sensing indices: a fuzzy c-means clustering approach. J Mountain Sci 14(10):2053–2063CrossRefGoogle Scholar
  7. 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 56:881–898CrossRefGoogle Scholar
  8. 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
  9. 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
  10. Cui P, Lin YM, Chen C (2012) Destruction of vegetation due to geo-hazards and its environmental impacts in the Wenchuan earthquake areas. Ecol Eng 44:61–69CrossRefGoogle Scholar
  11. De Vente J, Poesen J, Govers G, Boix-Fayos C (2009) The implications of data selection for regional erosion and sediment yield modelling. Earth Surf Process Landf 34:1994–2007CrossRefGoogle Scholar
  12. Deng Q, Qin F, Zhang B, Wang H, Luo M, Shu C, Liu H, Liu G (2015) Characterizing the morphology of gully cross-sections based on PCA: a case of Yuanmou Dry-Hot Valley. Geomorphology 228:703–713CrossRefGoogle Scholar
  13. 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, Parts A/B/C 67:145–152CrossRefGoogle Scholar
  14. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874CrossRefGoogle Scholar
  15. Fox GA, Sheshukov A, Cruse R, Kolar RL, Guertault L, Gesch KR, Dutnell RC (2016) Reservoir sedimentation and upstream sediment sources: perspectives and future research needs on streambank and gully erosion. Environ Manag 57(5):945–955CrossRefGoogle Scholar
  16. Geissen V, Kampichler C, López-de Llergo-Juárez JJ, Galindo-Acántara A (2007) Superficial and subterranean soil erosion in Tabasco, tropicalMexico: development of a decision tree modeling approach. Geoderma 139:277–287CrossRefGoogle Scholar
  17. Gholipour R, Jandaghi G, Rajaei R (2014) Contractor selection in MCDM context using fuzzy AHP. Iranian J Manag Stud 7(1):151–173Google Scholar
  18. Gόmez-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.  https://doi.org/10.1007/s11069-015-1703-0CrossRefGoogle Scholar
  19. Ibáñez J, Contador JL, Schnabel S, Valderrama JM (2016) Evaluating the influence of physical, economic and managerial factors on sheet erosion in rangelands of SW Spain by performing a sensitivity analysis on an integrated dynamic model. Sci Total Environ 544:439–449CrossRefGoogle Scholar
  20. Kheir RB, Wilson J, Deng Y (2007) Use of terrain variables for mapping gully erosion susceptibility in Lebanon. Earth Surface Process Landforms 32(12):1770–1782CrossRefGoogle Scholar
  21. McCloskey GL, Wasson RJ, Boggs GS, Douglas M (2016) Timing and causes of gully erosion in the riparian zone of the semi-arid tropical Victoria River, Australia: management implications. Geomorphology 266:96–104CrossRefGoogle Scholar
  22. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological and biological applications. Hydrol Process 5:3–30CrossRefGoogle Scholar
  23. Mukai S (2017) Gully erosion rates and analysis of determining factors: a case study from the semi-arid main Ethiopian Rift Valley. Land Degradation Dev 28(2):602–615CrossRefGoogle Scholar
  24. Nazari Samani A, Ahmadi H, Mohammadi A, Ghoddousi J, Salajegheh A, Boggs G, Pishyar R (2010) Factors controlling gully advancement and models evaluation (Hableh Rood Basin, Iran). Water Resour Manag 24(8):1531–1549CrossRefGoogle Scholar
  25. Nekhay O, Arriaza M, Boerboom L (2009) Evaluation of soil erosion risk using Analytic Network Process and GIS: a case study from Spanish mountain olive plantations. J Environ Manage 90:3091–3104CrossRefGoogle Scholar
  26. Pereira S, Zêzere JL, Bateira C (2012) Technical note: assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models. Nat Hazards Earth Syst Sci 12:979–988CrossRefGoogle Scholar
  27. Poesen J, Nachetergaele J, Verstraeten J, Valentin C (2003) Gully erosion and environmental change: importance and research needs. CATENA 50(2–4):91–133CrossRefGoogle Scholar
  28. Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ Earth Sci.  https://doi.org/10.1007/s12665-0154950-1
  29. Rahmati O, Zeinivand H, Besharat M (2015) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Hazards Risk.  https://doi.org/10.1080/19475705.2015.1045043CrossRefGoogle Scholar
  30. 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
  31. 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
  32. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12(1):77CrossRefGoogle Scholar
  33. Saaty RW (2003) Decision making in complex environment: the analytic hierarchy process (AHP) for decision making and the analytic network process (ANP) for decision making with dependence and feedback. Super Decisions, PittsburghGoogle Scholar
  34. Saaty TL (1996) Decision making with dependence and feedback: the analytic network process, vol 4922. RWS publications, PittsburghGoogle Scholar
  35. Samanlioglu F, Ayağ Z (2016) Fuzzy ANP-based PROMETHEE II approach for evaluation of machine tool alternatives. J Intelligent Fuzzy Syst 30(4):2223–2235CrossRefGoogle Scholar
  36. Serpa D, Nunes JP, Santos J, Sampaio E, Jacinto R, Veiga S, Lima JC, Moreira M, Corte-Real J, Keizer JJ, Abrantes N (2015) Impacts of climate and land use changes on the hydrological and erosion processes of two contrasting Mediterranean catchments. Science of the Total Environmental 538:64–77CrossRefGoogle Scholar
  37. 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
  38. Svoray T, Michailov E, Cohen A, Rokah L, Sturm A (2012) Predicting gully initiation: comparing data mining techniques, analytical hierarchy processes and the topographic threshold. Earth Surf Process Landforms 37:607–619CrossRefGoogle Scholar
  39. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293CrossRefGoogle Scholar
  40. Uygun Ö, Dede A (2016) Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Comput Ind Eng 102:502–511CrossRefGoogle Scholar
  41. Valentin C, Poesen J, Yong L (2005) Gully erosion: impacts, factors and control. CATENA 63:132–153CrossRefGoogle Scholar
  42. Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey. Ph.D Thesis Department of Geomatics the University of Melbourne, p 423Google Scholar
  43. Zakerinejad R, Maerker M (2015) An integrated assessment of soil erosion dynamics with special emphasis on gully erosion in the Mazayjan basin, southwestern Iran. Nat Hazards 79(1):25–50CrossRefGoogle Scholar
  44. Zhao Z, Chow TL, Rees HW, Yang Q, Xing Z, Meng FR (2009) Predict soil texture distributions using an artificial neural network model. Comput Electron Agric 65(1):36–48CrossRefGoogle Scholar
  45. Zucca C, Canu A, Della Peruta R (2006) Effects of land use and landscape on spatial distribution and morphological features of gullies in an agropastoral area in Sardinia (Italy). CATENA 68(2):87–95CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bahram Choubin
    • 1
  • Omid Rahmati
    • 2
    Email author
  • Naser Tahmasebipour
    • 3
  • Bakhtiar Feizizadeh
    • 4
  • Hamid Reza Pourghasemi
    • 5
  1. 1.Department of Watershed Management EngineeringSari University of Agricultural Sciences and Natural ResurgencesSariIran
  2. 2.Young Researchers and Elites Club, Khorramabad BranchIslamic Azad UniversityKhorramabadIran
  3. 3.Department of Watershed Management Engineering, Faculty of AgricultureLorestan UniversityKhorramabadIran
  4. 4.Department of Remote Sensing and GISUniversity of TabrizTabrizIran
  5. 5.Department of Natural Resources and Environmental EngineeringShiraz UniversityShirazIran

Personalised recommendations