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相关概念视频

Manipulation and Analysis01:21

Manipulation and Analysis

14
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Levels of Use of a GIS01:29

Levels of Use of a GIS

18
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
18
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

16
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Thematic Layering in GIS01:30

Thematic Layering in GIS

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In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
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Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Sustainable Development01:43

Sustainable Development

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As the human population continues to grow and use resources, we must be mindful of our planet’s natural limits. Sustainable development provides a pathway to maintain and improve human life now while also ensuring that future generations will have the resources that they need. The long-term success of sustainability efforts rests on understanding the interplay between human actions and ecological systems.
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相关实验视频

Updated: May 11, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Published on: July 24, 2016

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使用基于机器学习的场景建模开发可持续城市规划的决策支持系统.

Zi Wang1, Fang Ren2

  • 1The Faculty of Art & Design, Quzhou College of Technology, Quzhou, 324000, China. wonthard@163.com.

Scientific reports
|April 16, 2025
PubMed
概括

本研究介绍了一种使用机器学习和模糊逻辑的新型决策支持系统 (DSS),以应对复杂的城市规划挑战. 该系统将绿色城市化确定为可持续城市发展的最佳战略.

关键词:
q-rung模糊的套件可以设置.在 DSS 中使用 DSS.在埃鲁恩斯 (ERUNS) 举行会议.洛普科沃 (LOPCOW) 是一家公司.随机森林的递归特征消除随机森林的递归特征消除城市化的城市化.

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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相关实验视频

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科学领域:

  • 城市规划和发展
  • 环境科学 环境科学
  • 数据科学和人工智能数据科学和人工智能

背景情况:

  • 快速的城市化为可持续的城市发展带来了复杂的挑战,往往压倒了传统的规划方法.
  • 将环境,社会和经济因素纳入城市规划是传统方法难以实现的.

研究的目的:

  • 提出一个创新的决策支持系统 (DSS),利用机器学习和模糊的决策来克服城市规划的复杂性.
  • 为数据驱动的,可持续的城市发展决策提供框架.

主要方法:

  • 使用随机森林递归特征消除 (RF-RFE) 来从15个参数中选择显著的标准.
  • 采用对数百分比变化驱动的目标权重 (LOPCOW) 来分配标准权重.
  • 应用了基于相对效用和非线性标准化 (ERUNS) 方法的评估,使用q-rung模糊集 (q-ROFS) 来排名城市发展替代方案.

主要成果:

  • 确定了包括环境影响,能源效率,社会公平和经济可行性在内的关键标准.
  • q-ROFS框架有效地处理了决策中的不确定性和不准确性.
  • 绿色城市化成为最有利的发展选择.

结论:

  • 拟议的DSS有效地整合了机器学习和模糊的多标准决策,以实现强大的城市规划.
  • 该系统有助于为可持续城市发展做出明智决策.
  • 绿色城市化最符合可持续发展目标.