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基于机器学习的城市扩张评估,使用集成的多种危险和环境经济影响.
Anjar Dimara Sakti1,2, Albertus Deliar3,4, Dyah Rezqy Hafidzah2
1Geographic Information Sciences and Technology Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia.
Scientific reports
|June 11, 2024
概括
这项研究引入了一个新的城市扩张优先级指数,使用遥感和机器学习来管理印度尼西亚班敦的危险开发. 该指数确定了需要紧急政府干预以实现可持续城市规划的高风险地区.
科学领域:
- 城市和区域规划 城市和区域规划
- 环境科学 环境科学
- 地理信息系统 地理信息系统
背景情况:
- 由土地开发需求驱动的城市化导致环境问题.
- 不受控制的城市扩张造成了资源供需之间的不平衡.
- 对危险的城市扩张需要有效的管理策略.
研究的目的:
- 开发一个综合模型来评估和优先考虑危险的城市扩散管理.
- 使用遥感和机器学习创建一个独特的城市扩张优先级指数.
- 确定在班都根大都会地区干预的高优先级区域.
主要方法:
- 长期遥感数据的应用.
- 使用机器学习技术制定城市扩张优先级指数.
- 将人类经济活动,环境退化和多重灾害水平纳入指数.
主要成果:
- 在1993-2008年期间,人类经济活动增长最快 (172,776公).
- 1985年至1993年期间,环境恶化最严重.
- 在1993-2008年期间,多重危险地点的度最高.
- 西班牙邦都区,Cimahi,邦都区和东邦都区的城市区域的郊区被确定为最优先的地区.
结论:
- 开发的模型和指数为优先考虑危险的城市扩散管理提供了科学基础.
- 高优先级地区需要政府立即关注以减轻负面影响.
- 该模型通过高效的城市环境监测来支持可持续的城市发展和自然资源保护.
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