Skip to main content

Advertisement

SpringerLink
Log in

Identification of soil erosion-susceptible areas using fuzzy logic and analytical hierarchy process modeling in an agricultural watershed of Burdwan district, India

  • Original Article
  • Published:
Environmental Earth Sciences Aims and scope Submit manuscript

Abstract

Soil erosion is a natural process; it adversely impacts natural resources, agricultural activities, ecological systems, and environmental quality as it degrades landscapes and water quality, disrupts ecosystems, and intensifies hazards. Management strategies are needed that protect soil erosion in agricultural watersheds to achieve the sustainable land-use planning. This study maps soil erosion susceptibility using two GIS-based machine-learning approaches—analytical hierarchy process (AHP) and fuzzy logic modeling in the Kunur River Basin, West Bengal, India. Fifteen soil erosion conditioning variables were integrated with the modeling methods, remote sensing data, and GIS analysis. The relative importance of the conditioning variables was assessed for their capacities to predict susceptibility of locations to soil erosion. The soil erosion susceptibility maps generated from the two models used 70% of surveyed soil erosion sites. These models’ maps were validated with the characteristics of the remaining 30% of the soil erosion sites to produce a receiver operating characteristics curve. The results indicated that the fuzzy logic model has the higher prediction accuracy; the area under the curve (AUC) value was 91.4%. The AUC value of the AHP model was 89.7%. Both models indicated that study area contains regions of high to severe soil erosion susceptibility. Logistic regression was used to discern the variables’ importance in the assessment. Relief, NDVI, distance from a river, rainfall erosivity, and soil types were the most important variables. TWI, SPI, aspect, and a sediment transportation index were of least importance. Fuzzy-logic-generated SESMs can be effective tools to guide protective actions and land managers’ measures during the primary stages of soil erosion to control the development of soil degradation.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 7

Similar content being viewed by others

References

  • Akgun A, Sezer EA, Nefeslioglu HA, Gokceoglu C, Pradhan B (2012) An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm. Comput Geosci 38(1):23–34

    Google Scholar 

  • Amiri M, Pourghasemi HR, Ghanbarian GA, Afzali SF (2019) Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modelling and mapping using three machine learning algorithms. Geoderma 340:55–69

    Google Scholar 

  • Anderson JG (1971) Rocket measurement of OH in the mesosphere. J Geophys Res 76(31):7820–7824

    Google Scholar 

  • Aniya M (1985) Landslide-susceptibility mapping in the Amahata river basin, Japan. Ann Assoc Am Geogr 75(1):102–114

    Google Scholar 

  • Arabameri A, Pradhan B, Rezaei K, Yamani M, Pourghasemi HR, Lombardo L (2018a) Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function–logistic regression algorithm. Land Degrad Dev 29(11):4035–4049

    Google Scholar 

  • Arabameri A, Rezaei K, Pourghasemi HR, Lee S, Yamani M (2018b) GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique. Environ Earth Sci 77:628

    Google Scholar 

  • Arabameri A, Pradha B, Rezaei K (2019) Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. J Environ Manag 232:928–942

    Google Scholar 

  • Arekhi S, Niazi Y, Kalteh AM (2012) Soil erosion and sediment yield modelling using RS and GIS techniques: a case study, Iran. Arab J Geosci 5:285–296. https://doi.org/10.1007/s12517-010-0220-4

    Article  Google Scholar 

  • Arnoldus HMJ (1980) An approximation of the rainfall factor in the Universal Soil Loss Equation. In: De Boodt M, Gabriels D (eds) Assessment of Erosion. Wiley, New York, pp 127–132

    Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1/2):15–31

    Google Scholar 

  • Banerjee R, Srivastava PK, Pike AWG, Petropoulos GP (2018) Identification of painted rock-shelter sites using GIS integrated with a decision support system and fuzzy logic. ISPRS International Journal of Geo-Information. https://doi.org/10.3390/ijgi7080326

    Article  Google Scholar 

  • Bharath HA, Vinay S, Ramachandra TV (2014) Landscape dynamics modelling through integrated Markov, Fuzzy-AHP and cellular automata. In: The proceeding of international geoscience and remote sensing symposium (IEEE IGARSS 2014), July 13th–July 19th 2014, Quebec City convention centre, Quebec

  • Camilo DC, Lombardo L, Mai PM, Dou J, Huser R (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. Environ Model Softw 97:145–156

    Google Scholar 

  • Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252

    Google Scholar 

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

    Google Scholar 

  • Cerdà A, Rodrigo-Comino J, Giménez-Morera A, Novara A, Pulido M, Kapović-Solomun M, Keesstra SD (2017b) Policies can help to apply successful strategies to control soil and water losses. The case of chipped pruned branches (CPB) in Mediterranean citrus plantations. Land Use Policy 75:734–745

    Google Scholar 

  • Chen L, Wang J, Fu B, Qiu Y (2001) Land use change in a small catchment of northern Loess Plateau, China. Agric Ecosyst Environ 86:163–172

    Google Scholar 

  • Conoscenti C, Maggio CD, 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

    Google Scholar 

  • Conoscenti C, Agnesi V, Cama M, Caraballo-Arias NA, Rotigliano E (2018) Assessment of gully erosion susceptibility using multivariate adaptive regression splines and accounting for terrain connectivity. Land Degrad Dev 29:724–736

    Google Scholar 

  • Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (North of Yenice, NW Turkey) by fuzzy approach. Environ Geol 41:720–730

    Google Scholar 

  • García-Díaz A, Bienes R, Sastre B, Novara A, Gristina L, Cerdà A (2017) Nitrogen losses in vineyards under different types of soil groundcover. A field runoff simulator approach in central Spain. Agric Ecosyst Environ 236:256–267

    Google Scholar 

  • Gayen A, Pourghasemi HR (2019) Spatial modeling of gully erosion: a new ensemble of CART and GLM data-mining algorithms. In: Spatial modeling in GIS and R for earth and environmental science, pp 653–669

    Google Scholar 

  • Gayen A, Saha S (2017) Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: a study on Pathro river basin, Jharkhand, India. Model Earth Syst Environ 3(3):1123–1139

    Google Scholar 

  • Gayen A, Saha S (2018) Deforestation probable area predicted by logistic regression in Pathro river basin: a tributary of Ajay River. Spat Inf Res 26(1):1–9

    Google Scholar 

  • Gayen A, Saha S, Pourghasemi HR (2019) Soil erosion assessment using RUSLE model and its validation by FR probability model. Geocarto Int. https://doi.org/10.1080/10106049.2019.1581272

    Article  Google Scholar 

  • Ghose DK, Samantaray S (2019) Sedimentation process and its assessment through integrated sensor networks and machine learning process. In Computational intelligence in sensor networks, pp 473–488

    Google Scholar 

  • Hembram TK, Saha S (2018) Prioritization of sub-watersheds for soil erosion based on morphometric attributes using fuzzy AHP and compound factor in Jainti River basin, Jharkhand, Eastern India. Environ Dev Sustain. https://doi.org/10.1007/s10668-018-0247-3

    Article  Google Scholar 

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol A J Royal Meteorol Soc 25(15):1965–1978

    Google Scholar 

  • Hongchun ZHU, Guoan T, Kejian Q, Haiying L (2014) Extraction and analysis of gully head of loess plateau in china based on digital elevation model. China Geogr Sci 24(3):328–338

    Google Scholar 

  • Horton R (1945) Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Geol Soc Am Bull 56: 275–370

    Google Scholar 

  • Kayastha P, Dhital MR, DeSmedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408

    Google Scholar 

  • Keesstra S, Pereira P, Novara A, Brevik EC, Azorin-Molina C, Parras-Alcántara L, Jordán A, Cerdà A (2016) Effects of soil management techniques on soil water erosion in apricot orchards. Sci Total Environ 551–552:357–366

    Google Scholar 

  • Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28

    Google Scholar 

  • Kosmas C, Gerontidis ST, Marathianou M (2000) The effect of land use change on soils and vegetation over various lithological formations on Lesvos (Greece). CATENA 40:51–68

    Google Scholar 

  • Kropacek J, Schillaci C, Salvini R, Marker M (2016) Assessment of gully erosion in the Upper Awash, Central Ethiopian highlands based on a comparison of archived aerial photographs and very high resolution satellite images. GeografiaFisica e Dinamica Quaternaria 39:161–170

    Google Scholar 

  • Lal R (2001) Soil degradation by erosion. Land Degrad Dev 12(6):519–539

    Google Scholar 

  • Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40(9):1095–1113

    Google Scholar 

  • Lisetskii FN (2008) Agrogenic transformation of soils in the dry steppe zone under the impact of antique and recent land management practices. Eurasian Soil Sci 41(8):805–817

    Google Scholar 

  • Lombardo L, Mai PM (2018) Presenting logistic regression-based landslide susceptibility results. Eng Geol 244:14–24

    Google Scholar 

  • Malczewski J (1999) A GIS-based approach to multiple criteria group decision making. Int J Geograph Inf Syst 10:955–971

    Google Scholar 

  • Mendicino G (1999) Sensitivity analysis on GIS procedures for the estimate of soil erosion risk. Nat Hazards 20(2–3):231–253

    Google Scholar 

  • Mhazo N, Chivenge P, Chaplot V (2016) Tillage impact on soil erosion by water: discrepancies due to climate and soil characteristics. Agric Ecosyst Environ 230:231–241

    Google Scholar 

  • Mirmousavi SH (2016) Regional modeling of wind erosion in the North West and South West of Iran. Eurasian Soil Sci 49(8):942–953

    Google Scholar 

  • Moore ID, Burch GJ (1986) Physical basis of the length-slope factor in the universal soil Loss equation. Soil Science Soc Am J 50:1294–1298

    Google Scholar 

  • Moore ID, Wilson JP (1991) Length-slope factors for the revised universal soil loss equation: simplified method of estimation. J Soil Water Conserv 47(5):423–428

    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

    Google Scholar 

  • Morgan RPC (1996) Soil erosion and conservation, 2nd edn. Longman, Harlow

    Google Scholar 

  • Morgan RPC, Quinton JN, Smith RE, Govers G, Poesen JWA, Auerswald K, Chisci G, Torri D, Styczen ME (1998) The European soil erosion model (EUROSEM): a process based approach for predicting soil loss from fields and small catchments. Earth Surf Process Landf 23:527–544

    Google Scholar 

  • Nagarajan R, Roy A, Kumar RV, Mukherjee A, Khire MV (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Environ 58(4):275–287

    Google Scholar 

  • Oh JH, Jung SG (2005) Potential soil prediction for land resource management in the Nakdong River basin. J Korean Soc Rural Plan 11(2):9–19

    Google Scholar 

  • Park S, Oh S, Jeon S, Jung H, Choi C (2011) Soil erosion risk in Korean watersheds, assessed using the revised universal soil loss equation. J Hydrol 399(3–4):263–273

    Google Scholar 

  • Pavelsky TM, Smit LC (2008) RivWidth: a software tool for the calculation of river widths from remotely sensed imagery. IEEE Geosci Remote Sens Lett 5(1):70–73

    Google Scholar 

  • Pimentel D (2006) Soil erosion: a food and environmental threat. Environ Dev Sustain 8:119–137

    Google Scholar 

  • Poesen J, Nachtergaele J, Verstraeten G, Valentin C (2003) Gully erosion and environmental change: importance and research needs. CATENA 50(2–4):91–133

    Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2012) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. https://doi.org/10.1007/s12517-012-0532-7

    Article  Google Scholar 

  • Pradhan B (2010) Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Sp Res 45:1244–1256

    Google Scholar 

  • Pradhan B, Pirasteh S (2010) Comparison between prediction capabilities of neural network and fuzzy logic techniques for landslide susceptibility mapping. Disaster Adv 3(2):26–34

    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(2):1231–1258

    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

    Google Scholar 

  • Rashid T, Agrafiotis I, Nurse JR (2016) A new take on detecting insider threats: exploring the use of hidden markov models. In: Proceedings of the 8th ACM CCS International workshop on managing insider security threats, pp 47–56

  • Ren L, Huang J, Huang Q, Liang Y (2018) A fractal and entropy-based model for selecting the optimum spatial scale of soil erosion. Arab J Geosci 11(8):161

    Google Scholar 

  • Renard KG, Foster GR, Weesies GA, Porter JP (1991) RUSLE, revised universal soil loss equation. J Soil Water Conserv 46(1):30–33

    Google Scholar 

  • Rodrigo-Comino J, Cerdà A (2018) Improving stock unearthing method to measure soil erosion rates in vineyards. Ecol Indicator 85:509–517

    Google Scholar 

  • Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281

    Google Scholar 

  • Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill Book Co, New York, p 287

    Google Scholar 

  • Saaty TL, Vargas LG (2001) Models, methods, concepts and applications of the analytic hierarchy process. Kluwer, Dordrecht, p 333

    Google Scholar 

  • Saha AK, Gupta RP, Arora MK (2002) GIS-based landslide hazard zonation in the Bhagirathi (Ganga) valey, Himalayas. Int J Remote Sens 23(2):357–369

    Google Scholar 

  • Shahabi H, Khezri S, Ahmad BB, Hashim M (2014) Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models. CATENA 115:55–70

    Google Scholar 

  • Siriwardena L, Finlayson BL, McMahon TA (2006) The impact of land use change on catchment hydrology in large catchment: the Comet River, Central Queensland, Australia. J Hydrol 326:199–214

    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

    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 Process Landf 37:607–619

    Google Scholar 

  • Thornes JB (1985) The ecology of erosion. Geography 70:222–235

    Google Scholar 

  • Thornes JB (1990) Vegetation and erosion: processes and environments. Wiley, Chichester

    Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2011) Landslide susceptibility mapping at HoaBinh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci. https://doi.org/10.1016/j.cageo.2011.10.031

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in HoaBinh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. CATENA 96:28–40

    Google Scholar 

  • Valentin C, Poesen J, Li Y (2005) Gully erosion: impacts, factors and control. CATENA 63(2–3):132–153

    Google Scholar 

  • Wei W, Chen L, Fu B, Huang Z, Wu D, Gui L (2007) The effect of land uses and rainfall regimes on runoff and soil erosion in the semi-arid loess hilly area, China. J Hydrol 335:247–258

    Google Scholar 

  • Wentworth CK (1930) A simplified method of determining the average slope of land surface. Am J Sci 117:184–194

    Google Scholar 

  • Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: a guide to conservation planning. Agriculture handbook, vol 282. USDA-ARS, USA

  • Yanar TA (2003) The enhancement of the cell-based GIS Analysis with fuzzy processing Capabilities. MS thesis. The Middle East Technical University

  • Wu Q, Wang M (2007) A framework for risk assessment on soil erosion by water using an integrated and systematic approach. J Hydrol 337(1–2):11–21

    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

    Google Scholar 

Download references

Acknowledgements

Amiya Gayen would like to give sincere thanks to the assistant professor Dr. Swades Pal for his fair cooperation and cordial support to precede his research work. Dr. Hamid Reza Pourghasemi would like to thank the Shiraz University for providing Grant no. 96GRD1M271143 in the current study.

Author information

Authors and Affiliations

  1. Department of Geography, University of Gour Banga, Malda, West Bengal, 732103, India

    Sunil Saha

  2. Department of Geography, Ballygunge Science College, University of Calcutta, Kolkata, West Bengal, 700019, India

    Amiya Gayen

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

    Hamid Reza Pourghasemi

  4. Department of Geography, Texas State University, San Marcos, TX, 78666, USA

    John P. Tiefenbacher

Authors
  1. Sunil Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amiya Gayen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamid Reza Pourghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John P. Tiefenbacher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Reza Pourghasemi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saha, S., Gayen, A., Pourghasemi, H.R. et al. Identification of soil erosion-susceptible areas using fuzzy logic and analytical hierarchy process modeling in an agricultural watershed of Burdwan district, India. Environ Earth Sci 78, 649 (2019). https://doi.org/10.1007/s12665-019-8658-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12665-019-8658-5

Keywords

Search

Navigation