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Related Concept Videos

Manipulation and Analysis01:21

Manipulation and Analysis

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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

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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...
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Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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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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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

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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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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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Related Experiment Video

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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Developing a decision support system for sustainable urban planning using machine learning-based scenario modeling.

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
Summary
This summary is machine-generated.

This study introduces a novel decision support system (DSS) using machine learning and fuzzy logic to address complex urban planning challenges. The system identifies Green Urbanization as the optimal strategy for sustainable urban development.

Keywords:
q-rung fuzzy setsDSSERUNSLOPCOWRandom forest recursive feature eliminationUrbanization

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Area of Science:

  • Urban Planning and Development
  • Environmental Science
  • Data Science and Artificial Intelligence

Background:

  • Rapid urbanization presents complex challenges for sustainable urban development, often overwhelming conventional planning methods.
  • Integrating environmental, social, and economic factors into urban planning is difficult with traditional approaches.

Purpose of the Study:

  • To present an innovative decision support system (DSS) that leverages machine learning and fuzzy decision-making to overcome urban planning complexities.
  • To provide a framework for data-driven, sustainable urban development decisions.

Main Methods:

  • Utilized random forest recursive feature elimination (RF-RFE) for significant criterion selection from 15 parameters.
  • Employed logarithmic percentage change-driven objective weighting (LOPCOW) to assign criterion weights.
  • Applied the evaluation based on relative utility and nonlinear standardization (ERUNS) method with q-rung fuzzy sets (q-ROFS) for ranking urban development alternatives.

Main Results:

  • Identified key criteria including environmental impact, energy efficiency, social equity, and economic viability.
  • The q-ROFS framework effectively handled uncertainty and imprecision in decision-making.
  • Green Urbanization emerged as the most advantageous development option.

Conclusions:

  • The proposed DSS effectively integrates machine learning and fuzzy multi-criteria decision-making for robust urban planning.
  • The system facilitates informed decision-making for sustainable urban development.
  • Green Urbanization aligns best with sustainable development goals.