Skip to main content

Advertisement

SpringerLink
Log in

Spatial prediction of soil erosion susceptibility: an evaluation of the maximum entropy model

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Soil erosion is considered as the most widespread form of soil degradation which causes serious environmental problems. This study investigates the performance of the maximum entropy (ME) in mapping rill erosion susceptibility in the Golgol watershed, Ilam province, Iran. To this end, ten rill erosion conditioning factors were selected to be employed in the modelling process based on an investigation of the literature. These layers are: elevation, slope percent, aspect, stream power index, topographic wetness index, distance from streams, plan curvature, lithology, land use, and soil. Then, a training dataset of rill erosion locations was used for modelling this phenomenon. The area under receiver operating characteristics curve was used for evaluating the performance of the ME model. In addition, Modified Pacific South-West Inter Agency Committee (MPSIAC) framework was applied and sediment yield was determined for different hydrological units in the study area. At last, Jackknife test was implemented to show the contribution of the factors in the modelling process. The results depicted that area under ROC curve for training and validation datasets were 0.867, and 0.794, respectively. Therefore, this conclusion can be achieved that ME worked well and could be a good tool for generating rill erosion susceptibility maps and its output could be employed for soil conservation in similar areas.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Abdullah M, Feagin R, Musawi L (2017) The use of spatial empirical models to estimate soil erosion in arid ecosystems. Environ Monit Assess. https://doi.org/10.1007/s10661-017-5784-y

  • Amiri F (2010) Estimate of erosion and sedimentation in semi- arid basin using empirical models of erosion potential within a geographic information system. Air, Soil and Water Research 3:37

    Article  Google Scholar 

  • 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 262:61–76

    Article  Google Scholar 

  • Auerswald K, Fiener P, Dikau R (2009) Rates of sheet and rill erosion in Germany—A meta-analysis. Geomorphology 111(3):182–193

    Article  Google Scholar 

  • Bagherzadeh A, Daneshvar MRM (2013) Evaluation of sediment yield and soil loss by the MPSIAC model using GIS at Golestan watershed, northeast of Iran. Arab J Geosci 6(9):3349–3362

    Article  Google Scholar 

  • Bahadur KK (2009) Mapping soil erosion susceptibility using remote sensing and GIS: a case of the Upper Nam Wa Watershed, Nan Province, Thailand. Environ Geol 57(3):695–705

    Article  Google Scholar 

  • Bakker MM, Govers G, Kosmas C, Vanacker V, Van Oost K, Rounsevell M (2005) Soil erosion as a driver of land-use change. Agric Ecosyst Environ 105(3):467–481

    Article  Google Scholar 

  • Benzer N (2010) Using the geographical information systemand remote sensing techniques for soil erosion assessment. Pol J Environ Stud 19(5):881–886

    Google Scholar 

  • Cerdan O, Le Bissonnais Y, Souchère V, Martin P, Lecomete V (2002) Sediment con- centration in interrill flow: interactions between soil surface conditions, vegetation and rainfall. Earth Surf Process Landf 27(2):193–205. https://doi.org/10.1002/esp.314

    Article  Google Scholar 

  • Chang-Jo FC, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472

    Article  Google Scholar 

  • Chen W, Pourghasemi HR, Naghibi SA (2017a) A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bull Eng Geol Environ:1–18. https://doi.org/10.1007/s10064-017-1010-y

  • Chen W, Pourghasemi HR, Naghibi SA (2017b) Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms. Bull Eng Geol Environ:1–19. https://doi.org/10.1007/s10064-017-1004-9

  • 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 615:272–281

    Article  Google Scholar 

  • 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):881–898

    Article  Google Scholar 

  • Conoscenti C, Di Maggio C, Rotigliano E (2008) Soil erosion susceptibility assessment and validation using a geostatistical multivariate approach: a test in Southern Sicily. Nat Hazards 46(3):287–305

    Article  Google Scholar 

  • 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):1179–1195

    Article  Google Scholar 

  • 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 204:399–411

    Article  Google Scholar 

  • Csáfordi P, Pődör A, Bug J, Gribovsyki Z (2012) Soil erosion analysis in a small forested catchment supported by ArcGIS Model Builder. Acta Silvatica et Lignaria Hungarica 8(1):39–56

    Article  Google Scholar 

  • Daneshvar MRM, Bagherzadeh A (2012) Evaluation of sediment yield in PSIAC and MPSIAC models by using GIS at Toroq Watershed, Northeast of Iran. Front Earth Sci 6(1):83–94

    Article  Google Scholar 

  • Duarte L, Teodoro A, Gonçalves J, Soares D, Cunha M (2016) Assessing soil erosion risk using RUSLE through a GIS open source desktop and web application. Environ Monit Assess 188(6):1–16

    Article  Google Scholar 

  • Dumas P, Printemps J (2010) Assessment of soil erosion using USLE model and GIS for integrated watershed and coastal zone management in the South Pacific Islands. In: Proceedings Interpraevent, International Symposium in Pacific Rim, Taipei, pp. 856–866

  • Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explana- tion of Maxent for ecologists. Divers Distrib 17:43–57

    Article  Google Scholar 

  • Falah F, Ghorbani Nejad S, Rahmati O, Daneshfar M, Zeinivand H (2017) Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods. Geocarto Int 32(10):1069–1089

    Google Scholar 

  • Felicísimo ÁM, 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 10(2):175–189

    Article  Google Scholar 

  • 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(2):175–189

    Article  Google Scholar 

  • Feng X, Wang Y, Chen L, Fu B, Bai G (2010) Modeling soil erosion and its response to land-use change in hilly catchments of the Chinese Loess Plateau. Geomorphology 118(3):239–248

    Article  Google Scholar 

  • Feyznia S (1995) Rocks strength against erosion factors in different climates of Iran. Journal of the Natural Resources of Iran 47:95–116

    Google Scholar 

  • Geology Survey of Iran (GSI) (1997) Geology map of the Birjand Township. http://www.gsi.ir/Main/Lang_en/index.html. Accessed September 2000

  • Ghorbani Nejad S, Falah F, Daneshfar M, Haghizadeh A, Rahmati O (2017) Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models. Geocarto Int 32(2):167–187

    Google Scholar 

  • Govers G, Giménez R, Van Oost K (2007) Rill erosion: Exploring the relationship between experiments, modelling and field observations. Earth-Sci Rev 84(3–4):87–102

    Article  Google Scholar 

  • Haghizadeh A, Siahkamari S, Haghiabi AH, Rahmati O (2017) Forecasting flood-prone areas using Shannon’s entropy model. J Earth Syst Sci. https://doi.org/10.1007/s12040-017-0819-x

  • Hancock GR, Crawter D, Fityus SG, Chandler J, Wells T (2008) The measurement and modelling of rill erosion at angle of repose slopes in mine spoil. Earth Surf Process Landf 33(7):1006–1020

    Article  Google Scholar 

  • Harmon RS, Doe WW (2001) Landscape erosion and evolution modeling: Springer Science & Business Media

  • Harte J, Newman EA (2014) Maximum information entropy: a foundation for ecological theory. Trends Ecol Evol 29(7):384–389

    Article  Google Scholar 

  • Hong H, Naghibi SA, Moradi Dashtpagerdi M, Pourghasemi HR, Chen W (2017) A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arab J Geosci 10(7):167. https://doi.org/10.1007/s12517-017-2905-4

    Article  Google Scholar 

  • Ilanloo M (2012) Estimation of soil erosion rates usingMPSIAC models (Case Study Gamasiab basin). International Journal of Agriculture and Crop Sciences 4(16):1154–1158

    Google Scholar 

  • Kleidon A, Malhi Y, Cox PM (2010) Maximum entropy production in environmental and ecological systems. Philos T Roy Soc B 365(1545):1297–1302

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Kornejady A, Ownegh M, Rahmati O, Bahremand A (2017b) Landslide susceptibility assessment using three bivariate models considering the new topo-hydrological factor: HAND. Geocarto Int. https://doi.org/10.1080/10106049.2017.1334832

  • Koulouri M, Giourga C (2007) Land abandonment and slope gradient as key factors of soil erosion in Mediterranean terraced lands. Catena 69(3):274–281

    Article  Google Scholar 

  • Ledermann T, Herweg K, Liniger HP, Schneider F, Hurni H, Prasuhn V (2010) Applying erosion damage mapping to assess and quantify off-site effects of soil erosion in Switzerland. Land Degrad Dev 21(4):353–366

    Article  Google Scholar 

  • Lu H, Gallant J, Prosser IP, Moran C, Priestley G (2001) Prediction of sheet and rill erosion over the Australian continent, incorporating monthly soil loss distribution. Land and Water Technical Report. CSIRO, Canberra

    Google Scholar 

  • Mandal D, Sharda VN (2013) Appraisal of soil erosion risk in the Eastern Himalayan region of India for soil conservation planning. Land Degrad Dev 24(5):430–437

    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 

  • Medley KA (2010) Niche shifts during the global invasion of the Asian tiger mosquito, Aedes albopictus Skuse (Culicidae), revealed by reciprocal distribution models. Glob Ecol Biogeogr 19:122–123

    Article  Google Scholar 

  • Mekonnen M, Keesstra SD, Stroosnijder L, Baartman JE, Maroulis J (2015) Soil conservation through sediment trapping: a review. Land Degrad Dev 26(6):544–556

    Article  Google Scholar 

  • Meusburger K, Mabit L, Park J, Sandor T, Alewell C (2013) Combined use of stable isotopes and fallout radionu- clides as soil erosion indicators in a forested mountain site, South Korea. Biogeosciences 10(8):5627–5638

    Article  Google Scholar 

  • Ministry of Agriculture-Jahad (MAJ) (2014) The evaluation of land and resource planning, Report II, 283 p. http://www.maj.ir/Portal/Home/Default.aspx?CategoryID=92dd15a0-2395-43be-9859-c79116ab05da

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

    Article  Google Scholar 

  • Mullan D (2013) Soil erosion under the impacts of future climate change: Assessing the statistical significance of future changes and the potential on-site and off-site problems. Catena 109:234–246

    Article  Google Scholar 

  • Naghibi SA, Ahmadi K, Daneshi A (2017a) Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping. Water Resour Manag. https://doi.org/10.1007/s11269-017-1660-3

  • Naghibi SA, Moghaddam DD, Kalantar B, Pradhan B, Kisi O (2017b) A Comparative Assessment of GIS-Based Data Mining Models and a Novel Ensemble Model in Groundwater Well Potential Mapping. J Hydrol. https://doi.org/10.1016/j.jhydrol.2017.03.020

  • Naghibi SA, Pourghasemi HR, Abbaspour K (2017c) A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theor Appl Climatol. https://doi.org/10.1007/s00704-016-2022-4

  • Park NW (2015) Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets. Environ Earth Sci 73(3):937–949

    Article  Google Scholar 

  • Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2):161–175

    Article  Google Scholar 

  • Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modelling of species geographic distributions. Ecol Model 190:231–259

    Article  Google Scholar 

  • Pourghasemi HR, Rahmati O (2018) Prediction of the landslide susceptibility: which algorithm, which precision? CATENA 162:177–192

    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, Melesse AM (2016) Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan. Iran Sci Total Environ 568:1110–1123

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR (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 HR, Noormohamadi F (2016a) Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Nat Hazards 82(2):1231–1258

    Article  Google Scholar 

  • Rahmati O, Pourghasemi HR, Melesse AM (2016b) Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran. Catena 137:360–372

    Article  Google Scholar 

  • Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B (2017a) 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 

  • Rahmati O, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Feizizadeh B (2017b) Evaluation of different machine learning models for predicting and mapping the susceptibility of gully erosion. Geomorphology 298:118–137

    Article  Google Scholar 

  • Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O (2015) Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci Inf 8(4):867–883

    Article  Google Scholar 

  • Renard KG, Foster G, Weesies G, McCool D, Yoder D (1997) Predicting soil erosion by water: a guide to conser- vation planning with the Revised Universal Soil Loss Equation (RUSLE), vol 703. United States Department of Agriculture, Washington, DC

    Google Scholar 

  • Renschler CS, Harbor J (2002) Soil erosion assessment tools from point to regional scales—the role of geomorphologists in land management research and implementation. Geomorphology 47(2):189–209

    Article  Google Scholar 

  • Riksen MJPM, De Graaff J (2001) On-site and off-site effects of wind erosion on European light soils. Land Degrad Dev 12(1):1–11

    Article  Google Scholar 

  • Shahzeidi S, Entezari M, Gholami M, Dadashzadah Z (2012) Assessment rate of soil erosion by GIS (case study Varmishgan, Iran). Journal of Basic and Applied Scientific Research 2(12):13115–13121

    Google Scholar 

  • Sharma SK, Tiwari KN (2009) Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment. J Hydrol 374(3):209–222

    Article  Google Scholar 

  • Shi ZH, Fang NF, Wu FZ, Wang L, Yue BJ, Wu GL (2012) Soil erosion processes and sediment sorting associated with transport mechanisms on steep slopes. J Hydrol 454:123–130

    Article  Google Scholar 

  • Shrimali SS, Aggarwal SP, Samra JS (2001) Prioritizing erosion-prone areas in hills using remote sensing and GIS—a case study of the Sukhna Lake catchment, Northern India. Int J Appl Earth Obs 3(1):54–60

    Article  Google Scholar 

  • Siahkamari S, Haghizadeh A, Zeinivand H, Tahmasebipour N, Rahmati O (2017) Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto Int:1–15. https://doi.org/10.1080/10106049.2017.1316780

  • Taheri M, Landi A, Archangi B (2013) Using RS, GIS systems and MPSIAC model to produce erosion mapand to estimate sedimentation. International Journal of Agriculture: Research and Review 3(4):881–886

    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(1):79. https://doi.org/10.1007/s12517-015-2166-z

    Article  Google Scholar 

  • Vandekerckhove L, Poesen J, Oostwoud-Wijdenes D, Nachtergaele J, Kosmas D, Roxo MJ, Figueiredo TD (2000) Thresholds for gully initiation and sedimentation in Mediterranean Europe. Earth Surf Process Landf:1201–1220

  • Vorpahl P, Elsenbeer H, Märker M, Schröder B (2012) How can statistical models help to determine driving factors of landslides? Ecol Model 239:27–39

    Article  Google Scholar 

  • Wirtz S, Seeger M, Ries JB (2012) Field experiments for understanding and quantifica- tion of rill erosion processes. Catena 91:21–34

    Article  Google Scholar 

  • Yesilnacar E, Topal T (2005) Landslide susceptibility mapping: a com- parison of logistic regression and neural networks methods in a mediumscale study, Hendek region (Turkey). Eng Geol 79:251–266

    Article  Google Scholar 

  • Yilmaz C, Topal T, Suzen 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 

  • Zhao G, Mu X, Wen Z, Wang F, Gao P (2013) Soil erosion, conservation, and eco-environment changes in the loess plateau of China. Land Degrad Dev 24(5):499–510

    Google Scholar 

Download references

Acknowledgments

This manuscript was extracted from first author’s thesis (Ph.D degree). We thank Iranian Department of Water Resources Management (IDWRM) and department of Geological Survey of Iran (GSI) for providing necessary data and maps.

Author information

Authors and Affiliations

  1. Department of Watershed Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Maryam Pournader & Hasan Ahmadi

  2. Department of Reclamation of Arid and Mountainous Regions, University of Tehran, Karaj, Iran

    Sadat Feiznia

  3. Department of Watershed and Range Management, Ilam University, Ilam, Iran

    Haji Karimi

  4. Soil Conservation and Watershed Management Research Institute (SCWMRI), Tehran, Iran

    Hamid Reza Peirovan

Authors
  1. Maryam Pournader
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hasan Ahmadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sadat Feiznia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haji Karimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hamid Reza Peirovan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sadat Feiznia.

Additional information

Communicated by: H. A. Babaie

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pournader, M., Ahmadi, H., Feiznia, S. et al. Spatial prediction of soil erosion susceptibility: an evaluation of the maximum entropy model. Earth Sci Inform 11, 389–401 (2018). https://doi.org/10.1007/s12145-018-0338-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-018-0338-6

Keywords

Search

Navigation