Landslides are a great geohazard, which is caused due to the interrelation of environmental, climate, and man-made factors. This paper examines the relationship that exists among geography, environment, and landslides with reference to their effects on human beings, economic systems, and sustainable development. Particular attention is given to Tamil Nadu, where such a district as the Nilgiris is subjected to regular landslides because of steep landscapes, high rainfall, and other human-induced factors. The article summarizes the development of landslide susceptibility models, including the use of GIS, machine learning, and statistical tools to determine the areas of risk. It also looks at the socioeconomic impact of landslides, such as infrastructure destruction to livelihood losses, and emphasizes on the necessity of sustainable risk management measures. This paper suggests that scientific instruments could be used to supplement traditional knowledge and policy framework in a bid to establish an effective multi-disciplinary approach to landslide mitigation. The results also highlight the necessity to plan the area proactively, educate people, and manage the land sustainably to minimize the risk of landslides and increase the resilience of the vulnerable regions.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Conclusion: Integrating Geography, Environment, and Sustainability for Resilient Landslide Risk Management

  • M. V. Karunambigai,
  • R. M. Yuvaraj,
  • V. Gopal

摘要

Landslides are a great geohazard, which is caused due to the interrelation of environmental, climate, and man-made factors. This paper examines the relationship that exists among geography, environment, and landslides with reference to their effects on human beings, economic systems, and sustainable development. Particular attention is given to Tamil Nadu, where such a district as the Nilgiris is subjected to regular landslides because of steep landscapes, high rainfall, and other human-induced factors. The article summarizes the development of landslide susceptibility models, including the use of GIS, machine learning, and statistical tools to determine the areas of risk. It also looks at the socioeconomic impact of landslides, such as infrastructure destruction to livelihood losses, and emphasizes on the necessity of sustainable risk management measures. This paper suggests that scientific instruments could be used to supplement traditional knowledge and policy framework in a bid to establish an effective multi-disciplinary approach to landslide mitigation. The results also highlight the necessity to plan the area proactively, educate people, and manage the land sustainably to minimize the risk of landslides and increase the resilience of the vulnerable regions.