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<p>Landslides are recognized as major natural geological hazards in the mountainous region, and they are accountable for enormous human causalities, damage to properties, and environmental issues in the Teesta River basin, Sikkim, India. GIS approaches are widely used in landslide susceptibility mapping (LSM) that can help relevant authorities to mitigate landslide risk. The binary logistic regression is applied to estimate the landslide susceptibility zonation (LSZ) in the upper Teesta River basin areas. The landslide inventory data are subdivided into training data sets (70%) for applying algorithms in models and testing data sets (30%) for testing model accuracy. The LSZ mapping is designed after analyzing multicollinearity test of 14 landslide CFs and the result shows that the VIF value is less than 10, and TOL is greater than 0.1, respectively. There is no multicollinearity for the 14 conditioning landslides factors. The upper Teesta River basin is categorized into five groups: very low-to-very high landslide susceptibility zones. The results highlighted that most of the middle and southern parts of the study region are highly prone to landslides compared to the other parts. The susceptibility of landslide in the upper Teesta River basin areas validated by performing the Receiver Operating Characteristics (ROC) curve, which showed an 83% confidence level. The present research demonstrated landslide vulnerability circumstances for the Teesta River basin, Sikkim, an area prone to landslides, emphasizing the need for an effective mitigation and management roadmap.</p>

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Landslide vulnerability mapping using GIS-based statistical model for sustainable ecosystem management in the Himalayan region of Teesta River basin, India

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月22日

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  • Subodh Chandra Pal,
  • Tanmoy Biswas,
  • Sumit Ghorai,
  • Chaitanya Baliram Pande,
  • Aznarul Islam,
  • Abu Reza Md. Towfiqul Islam,
  • Edris Alam

摘要

Landslides are recognized as major natural geological hazards in the mountainous region, and they are accountable for enormous human causalities, damage to properties, and environmental issues in the Teesta River basin, Sikkim, India. GIS approaches are widely used in landslide susceptibility mapping (LSM) that can help relevant authorities to mitigate landslide risk. The binary logistic regression is applied to estimate the landslide susceptibility zonation (LSZ) in the upper Teesta River basin areas. The landslide inventory data are subdivided into training data sets (70%) for applying algorithms in models and testing data sets (30%) for testing model accuracy. The LSZ mapping is designed after analyzing multicollinearity test of 14 landslide CFs and the result shows that the VIF value is less than 10, and TOL is greater than 0.1, respectively. There is no multicollinearity for the 14 conditioning landslides factors. The upper Teesta River basin is categorized into five groups: very low-to-very high landslide susceptibility zones. The results highlighted that most of the middle and southern parts of the study region are highly prone to landslides compared to the other parts. The susceptibility of landslide in the upper Teesta River basin areas validated by performing the Receiver Operating Characteristics (ROC) curve, which showed an 83% confidence level. The present research demonstrated landslide vulnerability circumstances for the Teesta River basin, Sikkim, an area prone to landslides, emphasizing the need for an effective mitigation and management roadmap.