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GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique

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Abstract

Toroud Watershed in Semnan Province, Iran is a prone area to gully erosion that causes to soil loss and land degradation. To consider the gully erosion, a comprehensive map of gully erosion susceptibility is required as useful tool for decreasing losses of soil. The purpose of this research is to generate a reliable gully erosion susceptibility map (GESM) using GIS-based models including frequency ratio (FR), weights-of-evidence (WofE), index of entropy (IOE), and their comparison to an expert knowledge-based technique, namely, Analytic Hierarchy Process (AHP). At first, 80 gully locations were identified by extensive field surveys and Google Earth images. Then, 56 (70%) gully locations were randomly selected for modeling process, and the remaining 26 (30%) gully locations were used for validation of four models. For considering geo-environmental factors, VIF and tolerance indices are used and among 18 factors, 13 factors including elevation, slope degree, slope aspect, plan curvature, distance from river, drainage density, distance from road, lithology, land use/land cover, topography wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), and slope–length (LS) were selected for modeling aims. After preparing GESMs through the mentioned models, final maps divided into five classes including very low, low, moderate, high, and very high susceptibility. The receiver operating characteristic (ROC) curve and the seed cell area index (SCAI) as two validation techniques applied for assessment of the built models. The results showed that the AUC (area under the curve) in training data are 0.973 (97.3%), 0.912 (91.2%), 0.939 (93.9%), and 0.926 (92.6%) for AHP, FR, IOE, and WofE models, respectively. In contrast, the prediction rates (validating data) were 0.954 (95.4%), 0.917 (91.7), 0.925 (92.5%), and 0.921 (92.1%) for above models, respectively. Results of AUC indicated that four model have excellent accuracy in prediction of prone areas to gully erosion. In addition, the SCAI values showed that the produced maps are generally reasonable, because the high and very high susceptibility classes had very low SCAI values. The results of this research can be used in soil conservation plans in the study area.

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References

  • Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibilitymapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10:194

    Article  Google Scholar 

  • Al-Abadi AM (2017) Modeling of groundwater productivity in northeastern Wasit Governorate, Iraq using frequency ratio and Shannon’s entropy Models. Appl Water Sci 7: 699–716

    Article  Google Scholar 

  • Bathrellos GD, Gaki-Papanastassiou K, Skilodimou HD, Papanastassiou D, Chousianitis KG (2012) Potential suitability for urban planning and industry development by using natural hazard maps and geological—geomorphological parameters. Environ Earth Sci 66(2):537–548

    Article  Google Scholar 

  • Bathrellos GD, Karymbalis E, Skilodimou HD, Gaki-Papanastassiou K, Baltas EA (2016) Urban flood hazard assessment in the basin of Athens Metropolitan city, Greece. Environ Earth Sci 75(4):319

    Article  Google Scholar 

  • Bathrellos GD, Skilodimou HD, Chousianitis K, Youssef AM, Pradhan B (2017) Suitability estimation for urban development using multi-hazard assessment map. Sci Total Environ 575:119–134

    Article  Google Scholar 

  • Bogdanovic D, Nikolic D, Ilic I (2012) Mining Method Selection by Integrated AHP and PROMETHEE Method. Anais da Academia Brasileira de Ciências 84:219–233

    Article  Google Scholar 

  • Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8:948

    Article  Google Scholar 

  • Cerdà A, Giménez-Morera A, Bodí MB (2009) Soil and water losses from new citrus orchards growing on sloped soils in the western Mediterranean basin. Earth Surf Proc Land 34:1822–1830

    Article  Google Scholar 

  • Cerdà A, González-Pelayo O, Giménez-Morera A, Jordán A, Pereira P, Novara A, Brevik EC, Prosdocimi M, Mahmoodabadi M, Keesstra S, García Orenes F, Ritsema C (2015) The use of barley straw residues to avoid high erosion and runoff rates on persimmon plantations in Eastern Spain under low frequency high magnitude simulated rainfall events. Soil Res 54(2):154–165

    Article  Google Scholar 

  • Cerdà A, González-Pelayo O, Giménez-Morera A, Jordán A, Pereira P, Novara A, Brevik EC, Prosdocimi M, Mahmoodabadi M, Keesstra S, Orenes FG, Ritsema CJ (2016) Use of barley straw residues to avoid high erosion and runoff rates on persimmon plantations in Eastern Spain under low frequency-high magnitude simulated rainfall events. Soil Res 54(2):154–165

    Article  Google Scholar 

  • Cerdà A, Keesstra SD, Rodrigo-Comino J, Novara A, Pereira P, Brevik E, Jordán A (2017a) Runoff initiation, soil detachment and connectivity are enhanced as a consequence of vineyards plantations. J Environ Manag 202:268–275

    Article  Google Scholar 

  • Cerdà A, Rodrigo-Comino J, Giménez-Morera A, Keesstra SD (2017b) An economic, perception and biophysical approach to the use of oat straw as mulch in Mediterranean rainfed agriculture land. Ecol Eng 108:162–171

    Article  Google Scholar 

  • Chen W, Li W, Chai H, Hou E, Li X, Ding X (2016) GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China. Environ Earth Sci 75:63

    Article  Google Scholar 

  • Chitsaz N, Banihabib ME (2015) Comparison of different multi criteria decision-making models in prioritizing flood management alternatives. Water Resour Manag 29:2503–2525

    Article  Google Scholar 

  • Conforti M, Aucelli PC, Robustelli G, Scarciglia F (2011) Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy). Nat Hazard 56:881–898

    Article  Google Scholar 

  • Conoscenti C, Agnesi V, Angileri S, Cappadonia C, Rotigliano E, Ma¨rker M (2013) A GIS-based approach for gully erosion susceptibility modelling: a test in Sicily, Italy. Environ Earth Sci 70:1179–1195

    Article  Google Scholar 

  • Conoscenti C, Angileri S, Cappadonia C, Rotigliano E, Agnesi V, Ma¨rker M (2014) Gully erosion susceptibility assessment by means of GIS-based logistic regression: a case of Sicily (Italy). Geomorphology 204(1):399–411

    Article  Google Scholar 

  • Ding Q, Chen W, Hong H (2017) Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping. Geocarto Int 32(6):619–639

    Google Scholar 

  • 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 67:145–152

    Article  Google Scholar 

  • European Commission (EC): Communication from the Commission to the Council, the European Parliament, the European Economic and Social Committee and the Committee of the Regions (2006) Thematic Strategy for Soil Protection, COM 231 Final. Brussels

  • Geology Survey of Iran (GSI) (1997) http://www.gsi.ir/Main/Lang_en/index.html

  • Gόmez-Gutiérrez A, Schnabel S, Felicı´simo AM (2009) Modelling the occurrence of gullies in rangelands of southwest Spain. Earth Surf Process Landf 34:1894–1902

    Article  Google Scholar 

  • Golestani G, Issazadeh L, Serajamani R (2014) Lithology effects on gully erosion in Ghoori chay Watershed using RS & GIS. Int J Biosci 4(2):71–76

    Google Scholar 

  • Gómez-Gutiérrez A, 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):291–314

    Article  Google Scholar 

  • Griggs D, Stafford-Smith M, Gaffney O, Rockström J, Öhman MC, Shyamsundar P, Noble I (2013) Policy: sustainable development goals for people and planet. Nature 495(7441):305–307

    Article  Google Scholar 

  • Guo-liang D, Yong-shuang Z, Javed I, Zhi-hua Y, Xin Y (2017) Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. J Mt Sci 14(2):249–268

    Article  Google Scholar 

  • Haghizadeh A, Siahkamari S, Haghiabi AH, Rahamti O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci 126:39

    Article  Google Scholar 

  • Hayas A, Vanwalleghem T, Laguna A, Peña A, Giráldez JV (2017) Reconstructing long-term gully dynamics in Mediterranean agricultural areas. Hydrol Earth Syst Sci 21:235–249

    Article  Google Scholar 

  • Hong H, Chen W, Xu C, Youssef A, Pradhan B, Bui D (2017) Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy. Geocarto Int 32(2):139–154

    Google Scholar 

  • Iranian Department of Water Resources Management (IDWRM) (2013) Weather and climate report. http://www.thrw.ir/. Accessed 25 Jun 2013

  • Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol 11:909–926

    Article  Google Scholar 

  • Jiuchun Y, Shuwen Z, Liping C, Fei L, Tianqi L, Yan G (2017) Gully erosion regionalization of black soil area in Northeastern China. Chin Geogra 27:78–87

    Article  Google Scholar 

  • Keesstra SD, Bouma J, Wallinga J, Tittonell P, Smith P, Cerdà A, Montanarella L, Quinton JN, Pachepsky Y, van der Putten WH, Bardgett RD, Moolenaar S, Mol G, Jansen B, Fresco LO (2016) The significance of soils and soil science towards realization of the United Nations Sustainable Development Goals. Soil 2:111–128

    Article  Google Scholar 

  • Keesstra S, Nunes J, Novara A, Finger D, Avelar D, Kalantari Z, Cerdà A (2018) The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci Total Environ 610:997–1009

    Article  Google Scholar 

  • Kim Y, Chung ES (2013) Assessing climate change vulnerability with group multi-criteria decision making approaches. Climatic Chang 121(2):301–315

    Article  Google Scholar 

  • Kirchhoff M, Rodrigo Comino J, Seeger M, Ries JB (2017) Soil erosion in sloping vineyards under conventional and organic land use managements (Saar-Mosel valley, Germany). Cuadernos de Investigación Geográfica 43:119–140

    Article  Google Scholar 

  • Kornejady A, Heidari K, Nakhavali M (2015) Assessment of landslide susceptibility, semi-quantitative risk and management in the Ilam dam basin, Ilam. Iran Environ Resour Res 3(1):85–109

    Google Scholar 

  • Kornejady A, Ownegh M, Bahremand A (2017) Landslide susceptibility assessment using maximum entropy model with two different data sampling methods. Catena 152:144–162

    Article  Google Scholar 

  • Kuhnert PM, Henderson AK, Bartley R, Herr A (2010) Incorporating uncertainty in gully erosion calculations using the random forests modelling approach. Environmetrics 21:493–509

    Google Scholar 

  • Ligonja PJ, Shrestha RP (2015) Soil erosion assessment in kondoa eroded area in Tanzania using universal soil loss equation, geographic information systems and socioeconomic approach. Land Degrad Dev 26(4):367–379

    Article  Google Scholar 

  • Lo CP, Yeung AKW (2002) Concepts and techniques of geographic information system. Pearson Education Inc., New Jersey

    Google Scholar 

  • Macharis C, Springael J, Brucker KD, Verbeke A (2004) PROMETHEE and AHP: the design of operational synergies in multicriteria analysis, strengthening PROMETHEE with ideas of AHP. Eur J Oper Res 153:307–317

    Article  Google Scholar 

  • Manap MA, Nampak H, Pradhan B, Lee S, Sulaiman WNA, Ramli MF (2014) Application of probabilisticbased frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab J Geosci 7(2):711–724

    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(4):530–540

    Article  Google Scholar 

  • Mekonnen M, Keesstra SD, Baartman JE, Stroosnijder L, Maroulis J (2017) Reducing sediment connectivity throughman-made and natural sediment sinks in the minizr catchment, Northwest Ethiopia. Land Degrad Dev 28(2):708–717

    Article  Google Scholar 

  • Moore ID, Burch GJ (1986) Physical basis of the length-slopejuctor in the Universal soil loss equation. Soil Sci Soc Am J 50:1294–1298

    Article  Google Scholar 

  • Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5:3–30

    Article  Google Scholar 

  • Nefeslioglu HA, Duman TY, Durmaz S (2008) Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey). Geomorphology 94(3):401–418

    Article  Google Scholar 

  • Oh H, Lee S, Hong SM (2017) Landslide susceptibility assessment using frequency ratio technique with iterative random sampling. J Sens 1–21

  • Papadakis M, Karimalis A (2017) Producing a landslide susceptibility map through the use of analytic hierarchical process in finikas watershed, North Peloponnese, Greece. Am J Geogr Inf Syst 6(1A):14–22

    Google Scholar 

  • Pawluszek K, Borkowski A (2017) Impact of DEM-derived factors and analytical hierarchy process on landslide susceptibility mapping in the region of Ro_zno´w Lake, Poland. Nat Hazards 86:919–952

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: safarood basin, Iran. CATENA 97:71–84

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Moezzi KD (2013) A comparative assessment of prediction capabilities of Dempster-Shafer and Weights-of-evidence models in landslide susceptibility mapping using GIS. Geo, Nat Haz Risk 4(2):93–118

    Article  Google Scholar 

  • Pourghasemi HR, 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, Haghizadeh A, Pourghasemi HR, Noormohamadi F (2016) Gully erosion susceptibility mapping: the role of GIS based bivariate statistical models and their comparison. Nat Hazards 82:1231–1258

    Article  Google Scholar 

  • 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 579:913–927

    Article  Google Scholar 

  • Razavizadeh S, Solaiman K, Massironi M, Kavian A (2017) Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci 76:499

    Article  Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process. McGraw Hill, New York

    Google Scholar 

  • Saaty TL, Vargas GL (2001) Models, methods, concepts, and applications of the analytic ‎hierarchy process. Kluwer Academic Publisher, Boston

    Book  Google Scholar 

  • Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45(5):665–679

    Article  Google Scholar 

  • 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 Proc Land 37:607–619

    Article  Google Scholar 

  • Tahmassebipoor N, Rahmati O, Noormohamadi F, Lee S (2016) Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arab J Geosci 9:79

    Article  Google Scholar 

  • 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

  • Vidal AL, Sahin E, Martelli N, Berhoune M, Bonan B (2010) Applying AHP to select drugs to be produced by anticipation in chemotherapy compounding unit. J Exp Syst Appli, Adelphi 37(2):1528–1534

    Article  Google Scholar 

  • Wang Q, Li W (2017) A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping. Phys Geogr 38(4):318–337

    Article  Google Scholar 

  • Wang Q, Li W, Chen W, Bai H (2015) GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. J. Earth Syst. Sci 124(7):1399–1415

    Article  Google Scholar 

  • Water Resources Company of Semnan (WRCS) (2015) Precipitation and temperature reports. http://www.Semnanrw

  • Wen F, Xin-sheng W, Yan-bo C, Bin Z (2017) Landslide susceptibility assessment using the certainty factor and analytic hierarchy process. J Mt Sci 14(5):906–925

    Article  Google Scholar 

  • Wu Y, Li W, Wang Q, Liu Q, Yang D (2016) Landslide susceptibility assessment using frequency ratio, statistical index and certainty factor models for the Gangu County, China. Arab J Geosci 9:84

    Article  Google Scholar 

  • Xie Z, Chen G, Meng X, Zhang Y, Qiao L, Tan L (2017) A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China. Environ Earth Sci 76:313

    Article  Google Scholar 

  • Yacov Y (2011) Harmonizing the omnipresence of MCDM in technology, society, and policy. Chapter 2

  • Yilmaz C, Topal T, Süzen ML (2012) GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey). Environ Earth Sci 65(7):2161–2178

    Article  Google Scholar 

  • Zabihi M, Mirchooli F, Motevalli A, Darvishan AK, Pourghasemi HR, Zakeri MA, Sadighi F (2018) Spatial modelling of gully erosion in Mazandaran Province, northern Iran. Catena 161:1–13

    Article  Google Scholar 

  • 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 79:25–50

    Article  Google Scholar 

  • Zhou C, Ge L, Dongchen E, Hsingchung C (2005) A case study of using external DEM in InSAR DEM generation. Geo-spat Inform Sci 8(1):14–18. https://doi.org/10.1007/BF02826985

    Article  Google Scholar 

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Acknowledgements

The study was supported by College of Agriculture, Shiraz University (Grant No. 96GRD1M271143).

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Authors and Affiliations

  1. Physical Geography (Geomorphology), Tarbiat Modares University, Tehran, Iran

    Alireza Arabameri

  2. Faculty of Earth Sciences, Kharazmi University, Tehran, Iran

    Khalil Rezaei

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

    Hamid Reza Pourghasemi

  4. Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon, 34132, South Korea

    Saro Lee

  5. Physical Geography (Geomorphology), Tehran University, Tehran, Iran

    Mojtaba Yamani

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  1. Alireza Arabameri
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Arabameri, A., Rezaei, K., Pourghasemi, H.R. et al. GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique. Environ Earth Sci 77, 628 (2018). https://doi.org/10.1007/s12665-018-7808-5

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