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Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India

Author

Listed:
  • Rabin Chakrabortty

    (The University of Burdwan)

  • Subodh Chandra Pal

    (The University of Burdwan)

  • Mehebub Sahana

    (University of Manchester)

  • Ayan Mondal

    (The University of Burdwan)

  • Jie Dou

    (China Three Gorges University
    Nagaoka University of Technology)

  • Binh Thai Pham

    (Duy Tan University)

  • Ali P. Yunus

    (Chengdu University of Technology)

Abstract

Land degradation is very severe in the subtropical monsoon-dominated region due to the uncertainty of rainfall in the long term, and most of the rainfall occurs with high intensity and kinetic energy over short time periods. So, keeping this scenario in view, the main objective of this work is to identify areas vulnerable to soil erosion and propose the most suitable model for soil erosion susceptibility in subtropical environment. The implementation of machine learning and artificial intelligence techniques with a GIS environment for determining erosion susceptibility is highly acceptable in terms of optimal accuracy. The point-specific values of different elements from random sampling were considered for this study. Sensitivity analysis of the predicted models (i.e., analytical neural network, geographically weighted regression and GWR–ANN ensemble) was performed using the maximum causative factors and related primary field observations. The area under curve of receiver operating system reveals precision with 87.13, 89.57 and 91.64 for GWR, ANN and ensemble GWR–ANN, respectively. The ensemble GWR–ANN is more optimal than the GWR, ANN for determining water-induced soil erosion susceptibility. The process of soil erosion is not a unidirectional process, so the multidimensional impacts from the conditioning factors have to be determined precisely by considering the maximum possible factors as well as selecting optimal models for specific regions.

Suggested Citation

  • Rabin Chakrabortty & Subodh Chandra Pal & Mehebub Sahana & Ayan Mondal & Jie Dou & Binh Thai Pham & Ali P. Yunus, 2020. "Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(2), pages 1259-1294, November.
  • Handle: RePEc:spr:nathaz:v:104:y:2020:i:2:d:10.1007_s11069-020-04213-3
    DOI: 10.1007/s11069-020-04213-3
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    References listed on IDEAS

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    1. Tamal Kanti Saha & Swades Pal, 2019. "Emerging conflict between agriculture extension and physical existence of wetland in post-dam period in Atreyee River basin of Indo-Bangladesh," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1485-1505, June.
    2. Swades Pal & Sandipta Debanshi, 2018. "Influences of soil erosion susceptibility toward overloading vulnerability of the gully head bundhs in Mayurakshi River basin of eastern Chottanagpur Plateau," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(4), pages 1739-1775, August.
    3. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical Tests for Spatial Nonstationarity Based on the Geographically Weighted Regression Model," Environment and Planning A, , vol. 32(1), pages 9-32, January.
    4. Massimo Conforti & Pietro Aucelli & Gaetano Robustelli & Fabio Scarciglia, 2011. "Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (Northern Calabria, Italy)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 56(3), pages 881-898, March.
    5. A. Carrara & F. Guzzetti & M. Cardinali & P. Reichenbach, 1999. "Use of GIS Technology in the Prediction and Monitoring of Landslide Hazard," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 20(2), pages 117-135, November.
    6. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
    7. Seyed Naghibi & Hamid Pourghasemi, 2015. "A Comparative Assessment Between Three Machine Learning Models and Their Performance Comparison by Bivariate and Multivariate Statistical Methods in Groundwater Potential Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5217-5236, November.
    8. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
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    Cited by:

    1. Amlan Ghosh & Sayandeep Rakshit & Suvarna Tikle & Sandipan Das & Uday Chatterjee & Chaitanya B. Pande & Abed Alataway & Ahmed A. Al-Othman & Ahmed Z. Dewidar & Mohamed A. Mattar, 2022. "Integration of GIS and Remote Sensing with RUSLE Model for Estimation of Soil Erosion," Land, MDPI, vol. 12(1), pages 1-15, December.
    2. Sukanta Malakar & Abhishek K. Rai & Arun K. Gupta, 2023. "Earthquake risk mapping in the Himalayas by integrated analytical hierarchy process, entropy with neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 951-975, March.
    3. Fahimeh Mirchooli & Maziar Mohammadi & Seyed Hamidreza Sadeghi, 2023. "Spatial modeling of relationship between soil erosion factors and land-use changes at sub-watershed scale for the Talar watershed, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3703-3723, April.
    4. Sumedh R. Kashiwar & Manik Chandra Kundu & Usha R. Dongarwar, 2022. "Soil erosion estimation of Bhandara region of Maharashtra, India, by integrated use of RUSLE, remote sensing, and GIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 937-959, January.
    5. Rabin Chakrabortty & Subodh Chandra Pal & Alireza Arabameri & Phuong Thao Thi Ngo & Indrajit Chowdhuri & Paramita Roy & Sadhan Malik & Biswajit Das, 2022. "Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3518-3557, March.

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