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<p>Together with ongoing urbanization and demographic expansion in moderate-sized Indian cities, solid waste management has become more challenging, making effective landfill site identification critical for sustainable solid waste management. This study presents a combined framework for landfill planning decision-support that integrates GIS-based multi-criteria decision analysis, regulatory restrictions, statistical validation, and sensitivity analysis. Following CPHEEO guidelines, Boolean constraint for five criteria was applied to eliminate environmentally restricted regions. Another set of six criteria was evaluated by AHP to create Landfill Suitability Index, resulting in a consistent weighting scheme (CR = 0.017). To reduce subjectivity, entropy-derived weights were determined to generate suitability map. Statistical comparison using Pearson correlation (<i>r</i> = 0.963) and t-test verified strong agreement between two weighting methods. Two final sites fulfilling all requirements were identified from combined suitable area (CSA). Model stability was evaluated through an OFAT sensitivity analysis with variations of six criteria weights (± 5–±20%), leading to 49 perturbations. The sensitivity results showed limited changes in CSA, and spatial stability of final sites, confirmed model robustness. The approach supports landfill planning under regulatory compliance, ensuring adequate capacity, and robustness against the uncertainty in weights; also provides a reproducible decision framework for sustainable solid waste management in emerging urban environments.</p>

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Integrated decision-support framework for landfill site selection using GIS-based multi-criteria analysis and sensitivity assessment: A case study of Bhiwani city, Haryana, India

  • 期刊论文

  • 发表日期：

    2026年7月28日

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  • Shefali Kundu,
  • Jitender Singh Laura,
  • Geeta Dhania,
  • Pinkey Sheoran

摘要

Together with ongoing urbanization and demographic expansion in moderate-sized Indian cities, solid waste management has become more challenging, making effective landfill site identification critical for sustainable solid waste management. This study presents a combined framework for landfill planning decision-support that integrates GIS-based multi-criteria decision analysis, regulatory restrictions, statistical validation, and sensitivity analysis. Following CPHEEO guidelines, Boolean constraint for five criteria was applied to eliminate environmentally restricted regions. Another set of six criteria was evaluated by AHP to create Landfill Suitability Index, resulting in a consistent weighting scheme (CR = 0.017). To reduce subjectivity, entropy-derived weights were determined to generate suitability map. Statistical comparison using Pearson correlation (r = 0.963) and t-test verified strong agreement between two weighting methods. Two final sites fulfilling all requirements were identified from combined suitable area (CSA). Model stability was evaluated through an OFAT sensitivity analysis with variations of six criteria weights (± 5–±20%), leading to 49 perturbations. The sensitivity results showed limited changes in CSA, and spatial stability of final sites, confirmed model robustness. The approach supports landfill planning under regulatory compliance, ensuring adequate capacity, and robustness against the uncertainty in weights; also provides a reproducible decision framework for sustainable solid waste management in emerging urban environments.