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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

36
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...
36
Manipulation and Analysis01:21

Manipulation and Analysis

18
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...
18
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

39
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...
39
Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

38
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
38
Levels of Use of a GIS01:29

Levels of Use of a GIS

41
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...
41
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

24
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...
24

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相关实验视频

Updated: May 30, 2025

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
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使用地理空间人工智能 (GeoAI) 改进易受洪水影响的地区绘制地图:一种非参数算法,增强了基于数学的元启发算法.

Seyed Vahid Razavi-Termeh1, Abolghasem Sadeghi-Niaraki1, Farman Ali2

  • 1Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea.

Journal of environmental management
|January 28, 2025
PubMed
概括
此摘要是机器生成的。

这项研究通过结合决策树 (DT) 和元启发算法来增强洪水易感性映射 (FSM). DT-Chaos游戏优化 (DT-CGO) 模型显著提高了准确性,提供了更好的洪水风险评估和减缓计划.

关键词:
易受洪水影响的地区.地理空间的人工智能 (GeoAI)机器学习 机器学习超音速算法的算法 超音速算法的算法这是卫星图像.

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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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Watershed Planning within a Quantitative Scenario Analysis Framework
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09:44

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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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科学领域:

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

背景情况:

  • 洪水对人类安全和基础设施构成重大风险,需要准确地绘制易受洪水影响的地区地图,以有效地减轻风险.
  • 目前用于洪水易感性映射 (FSM) 的机器学习模型面临的局限性包括数据依赖性,可解释性问题和过度装配.

研究的目的:

  • 通过将决策树 (DT) 算法与先进的元启发式优化技术集成,提高洪水易感性映射 (FSM) 的准确性和可靠性.
  • 为了评估DT的性能与梯度基础优化器 (GBO),混乱游戏优化 (CGO) 和算术优化算法 (AOA) 结合使用FSM.

主要方法:

  • 开发了四种模型:基本的决策树 (DT),DT-算术优化算法 (DT-AOA),DT-梯度基础优化器 (DT-GBO) 和DT-混乱游戏优化 (DT-CGO).
  • 评估模型使用统计指标,如根平均平方误差 (RMSE),平均绝对误差 (MAE),确定系数 (R2) 和曲线下的面积 (AUC).

主要成果:

  • DT-CGO模型表现出卓越的性能,在训练组中实现了最低的RMSE (0.17) 和MAE (0.06),以及最高的R2 (0.871) 和AUC (0.978).
  • 所有增强模型 (DT-AOA,DT-GBO,DT-CGO) 在测试组中都超过了基本的DT模型,其中DT-CGO显示了最高的有效性.
  • 优化的模型表现出强大的预测能力,验证了它们在洪水易感性映射中的有效性.

结论:

  • 将决策树算法与元启发式优化算法相结合,可显著提高洪水易感性绘图的准确性.
  • DT-CGO模型提供了一种强大而准确的方法来识别易受洪水影响的地区,为决策者提供关键数据.
  • 准确的FSM对于制定有效的洪水缓解策略和减少灾害影响至关重要.