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相关概念视频

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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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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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Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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相关实验视频

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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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一个可解释的深度学习模型来绘制土地沉降危险的地图.

Paria Rahmani1, Hamid Gholami2, Shahram Golzari3,4

  • 1Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.

Environmental science and pollution research international
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PubMed
概括

这项研究提高了深度学习 (DL) 模型的可解释性,用于土地沉降 (LS) 危险映射. 关键特征,如距离井和DEM的距离,显著影响LS预测准确度.

关键词:
深度学习是一种深度学习.游戏理论的游戏理论.可以解释性 解释性土地沉降的情况这就是 SHAP SHAP 的意思.

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科学领域:

  • 地质科学 地质科学
  • 人工智能的人工智能
  • 环境科学 环境科学

背景情况:

  • 土地沉降 (LS) 构成重大地缘危险,需要准确的预测模型.
  • 深度学习 (DL) 模型为绘制LS易受性的强大工具,但往往缺乏可解释性.
  • 了解驱动LS的因素对于有效的风险管理和缓解策略至关重要.

研究的目的:

  • 解释深度学习模型 (CNN和LSTM) 对土地易受沉降危险映射的输出.
  • 通过先进的解释技术,识别和排名控制土地沉降的最有影响力的特征.
  • 提高基于DL的地缘危险评估的透明度和可靠性.

主要方法:

  • 使用现场工作和存在点创建了土地沉降 (LS) 的库存地图.
  • 粒子集群优化 (PSO) 确定了DL模型 (CNN和LSTM) 的11个关键特征.
  • 为了分析模型输出,采用了包括SHAP和PFIM在内的六种解释方法.

主要成果:

  • 无论是CNN还是LSTM模型,在绘制LS危险的过程中都取得了极好的准确性 (AUC>0.90).
  • 从井的距离,GDR和DEM被确定为影响DL模型预测的前三大最有影响力的特征.
  • 布地块显示,距离井和粗碎片的距离对LS产生了负面影响,而土地使用和DEM有积极的影响.

结论:

  • 应用的解释技术有效地解决了DL模型在地理危险评估中的"黑子"性质.
  • 特性重要性分析为土地沉降的驱动因素提供了宝贵的见解.
  • 这项研究表明,可解释的DL对于强大的土地沉降危险映射的实用性.