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

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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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...
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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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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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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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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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绘制地图沿海转型与一个新的细胞自动机-马科夫-随机森林框架用于土地使用变化建模.

Mohammad Reza Nikoo1, Erfan Zarei2, Malik Al-Wardy3

  • 1Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.

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概括

准确的沿海变化预测对于可持续管理至关重要. 本研究通过使用混合CA-马尔科夫和机器学习模型,增强了阿曼的土地使用/土地覆盖 (LULC) 和海岸线预测,提高了未来规划的准确性.

关键词:
这就是CAMarkov.混合建模混合建模预测LULC的预测恩德威 NDWI 恩德威 NDWI 恩德威随机的森林随机的森林海岸线变化改变了海岸线.

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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.
  • 沿海地区的管理

背景情况:

  • 沿海地区面临来自自然过程和人类活动的动态变化.
  • 准确预测海岸线和土地利用/土地覆盖 (LULC) 变化对于可持续的沿海管理至关重要.
  • 奥曼的沿海地区特别容易受到这些动态变化的影响.

研究的目的:

  • 开发和评估一个混合建模框架,将CA-Markov和机器学习结合起来,用于增强LULC和阿曼海岸线变化预测.
  • 评估与传统的CA-马尔科夫模型相比,不同混合模型的预测性能.
  • 为未来沿海LULC和海岸线动态提供准确的预测,以支持可持续的管理策略.

主要方法:

  • 使用多时间Landsat图像 (1997-2024) 和规范差水指数划定海岸线.
  • 使用终点速率和线性回归速率分析量化沿海侵蚀和积累速率.
  • 对未来LULC预测的四个模型 (CA-Markov,CA-Markov+XGBoost,CA-Markov+CART,CA-Markov+RF) 的评估,其中CA-Markov+RF显示出优异的性能.

主要成果:

  • 在1997年至2024年期间观察到海岸线变化的显著空间变化,其中可见的侵蚀在Rakhyut (-1.81米/年) 和Bawshar (1.41米/年) 的增长.
  • 检测到快速的城市扩张,特别是在马斯喀特,建筑面积从10.31平方公里 (1997年) 增加到116.41平方公里 (2015年).
  • 与CA-Markov (0.905) 相比,混合CA-Markov+RF模型实现了最高的预测精度 (0.935),证明了机器学习集成的有效性.

结论:

  • 混合CA-Markov+RF模型显著提高了LULC和脆弱沿海地区海岸线变化预测的准确性.
  • 未来的预测 (2033) 表明萨拉拉和索哈尔的城市持续增长,干旱地区的植被覆盖面可能会减少.
  • 这些发现强调了先进的建模技术对于有效的沿海区域管理和可持续发展规划的重要性.