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Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM)

Author

Listed:
  • Shabnam Mehrnoor

    (Islamic Azad University)

  • Maryam Robati

    (Islamic Azad University)

  • Mir Masoud Kheirkhah Zarkesh

    (Islamic Azad University)

  • Forough Farsad

    (Islamic Azad University)

  • Shahram Baikpour

    (Islamic Azad University)

Abstract

Land subsidence is a morphological phenomenon, which causes negative environmental and economic consequences for human societies. Therefore, identifying the areas prone to subsidence can be one of the necessary measures for reducing the potential risks. This study aims to evaluate the efficiency of the support vector machine (SVM) algorithm and weighted overlay index (WOI) models in zoning the rate of land subsidence hazard in Hashtgerd plain, Iran. First, the 19 criteria include groundwater depletion, groundwater extraction, aquifer thickness, alluvium thickness, aquifer recharge, well density, drainage density, groundwater depth, lithology, bedrock depth, average annual precipitation, average annual temperature, climate type, agricultural use, urban use, industrial use, distance from rivers and streams, distance from roads, distance from faults were considered. Then, the layers were weighed based on the Best–Worst Method (BWM). The results of BWM indicated that the factors of groundwater extraction (0.219), lithology (0.157), and groundwater depletion (0.079) have a greater effect on the potential for subsidence hazard. Moreover, the results of validation by performing ROC curve showed that the accuracy of RBF-SVM, LN-SVM, SIG-SVM, PL-SVM, and WOI were 95.7, 94.3, 94.9, 93.2, and 90%, respectively. Based on the ROC results, all of the models for preparing the subsidence hazard map in Hashtgerd plain exhibit excellent accuracy. Therefore, all of the models used here can predict the areas vulnerable to subsidence properly. In this study, the five land subsidence hazard maps were used as new input factors and integrated using fuzzy gamma-ensemble methods to make better hazard maps. The results of the ensemble model indicated that 19.3% of Hashtgerd plain is in the zone of high to very high sensitivity. The results of this study can help planners in managing and reducing the possible hazards of subsidence.

Suggested Citation

  • Shabnam Mehrnoor & Maryam Robati & Mir Masoud Kheirkhah Zarkesh & Forough Farsad & Shahram Baikpour, 2023. "Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM)," 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. 115(3), pages 1997-2030, February.
    • Handle: RePEc:spr:nathaz:v:115:y:2023:i:3:d:10.1007_s11069-022-05624-0
    DOI: 10.1007/s11069-022-05624-0
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    References listed on IDEAS

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    1. Rajat Agarwal & P. Garg, 2016. "Remote Sensing and GIS Based Groundwater Potential & Recharge Zones Mapping Using Multi-Criteria Decision Making Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 243-260, January.
    2. Biswajeet Pradhan & Mohammed Abokharima & Mustafa Jebur & Mahyat Tehrany, 2014. "Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in 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. 73(2), pages 1019-1042, September.
    3. Lei Nie & Min Zhang & Heqing Jian, 2013. "Analysis of surface subsidence mechanism and regularity under the influence of seism and fault," 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. 66(2), pages 773-780, March.
    4. Yang Yu & Shen-En Chen & Ka-Zhong Deng & Peng Wang & Hong-Dong Fan, 2018. "Subsidence Mechanism and Stability Assessment Methods for Partial Extraction Mines for Sustainable Development of Mining Cities—A Review," Sustainability, MDPI, vol. 10(1), pages 1-21, January.
    5. Bijuan Huang & Longcang Shu & Y. Yang, 2012. "Groundwater Overexploitation Causing Land Subsidence: Hazard Risk Assessment Using Field Observation and Spatial Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4225-4239, November.
    6. M. Sayyaf & M. Mahdavi & O. Barani & S. Feiznia & B. Motamedvaziri, 2014. "Simulation of land subsidence using finite element method: Rafsanjan plain case study," 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. 72(2), pages 309-322, June.
    7. Massimo Conforti & Francesco Muto & Valeria Rago & Salvatore Critelli, 2014. "Landslide inventory map of north-eastern Calabria (South Italy)," Journal of Maps, Taylor & Francis Journals, vol. 10(1), pages 90-102, January.
    8. Rajat Agarwal & P. K. Garg, 2016. "Remote Sensing and GIS Based Groundwater Potential & Recharge Zones Mapping Using Multi-Criteria Decision Making Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 243-260, January.
    9. Dieu Tien Bui & Biswajeet Pradhan & Owe Lofman & Inge Revhaug, 2012. "Landslide Susceptibility Assessment in Vietnam Using Support Vector Machines, Decision Tree, and Naïve Bayes Models," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-26, July.
    10. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    11. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    12. Iwan Rudiarto & Wiwandari Handayani & Jawoto Sih Setyono, 2018. "A Regional Perspective on Urbanization and Climate-Related Disasters in the Northern Coastal Region of Central Java, Indonesia," Land, MDPI, vol. 7(1), pages 1-15, March.
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