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相关概念视频

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

65
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...
65
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

42
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
42
Manipulation and Analysis01:21

Manipulation and Analysis

23
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...
23
Levels of Use of a GIS01:29

Levels of Use of a GIS

47
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...
47
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
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...
27
Thematic Layering in GIS01:30

Thematic Layering in GIS

35
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)...
35

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相关实验视频

Updated: Jun 20, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Watershed Planning within a Quantitative Scenario Analysis Framework

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整合机器学习和地理空间数据分析,以进行全面的洪水危险评估.

Chiranjit Singha1, Vikas Kumar Rana2, Quoc Bao Pham3

  • 1Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati (A Central University), Sriniketan, Birbhum, West Bengal, 731236, India.

Environmental science and pollution research international
|July 19, 2024
PubMed
概括

这项研究开发了一种先进的机器学习框架,用于评估印度西孟加拉邦的洪水危险. 结果显示,海拔和降水是关键因素,17.2-18.6%的地区高度易受洪水影响.

关键词:
评估洪水的情况.造成洪水的条件因素.机器学习 机器学习遥感是一种远程传感.

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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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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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相关实验视频

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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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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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科学领域:

  • 环境科学 环境科学
  • 地理空间分析的研究.
  • 机器学习 机器学习

背景情况:

  • 洪水是一个重要的全球性自然危险,气候变化加剧了.
  • 强大的洪水风险建模对于抗灾和适应战略至关重要.
  • 印度西孟加拉邦的阿兰巴格地区面临着大量的洪水风险.

研究的目的:

  • 开发和评估用于洪水危险评估的先进机器学习框架.
  • 确定影响研究区域洪水易感性的关键条件因素.
  • 为改善灾害管理和城市规划绘制洪水危险水平的地图.

主要方法:

  • 利用多种来源的地理空间数据集,包括Sentinel-1 SAR和全球洪水数据库进行洪水清单.
  • 纳入了十五个洪水条件因素:地形,土地覆盖,土壤,降雨量,近距离和人口统计.
  • 训练并测试各种机器学习模型 (RF,AdaBoost,XGB等) 使用特征选择和可解释性方法 (SHAP,Boruta).

主要成果:

  • 机器学习模型实现了超过80%的预测准确性 (AUC>0.80),随机森林 (RF) 和AdaBoost表现强.
  • 降雨和海拔被确定为导致洪水危险的最重要因素.
  • 研究发现,平均17.2%至18.6%的研究区对洪水风险高度敏感.

结论:

  • 开发的机器学习框架有效评估洪水危险,识别关键影响因素.
  • 研究区域的南部地区具有很高的洪水易感性,影响了基础设施和耕地.
  • 这些发现支持加强水力和水文建模,以有效地管理和减轻洪水风险.