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Statistical Significance01:50

Statistical Significance

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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

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Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Machines01:19

Machines

577
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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Introduction to Statistics01:17

Introduction to Statistics

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The science of statistics involves collecting, analyzing, interpreting, and presenting data. The method of collecting, organizing, and summarizing data is called descriptive statistics. The systematic method of drawing inferences from the sample data and predicting unknown characteristics of a population is called inferential statistics.
In statistics, the collection of individuals or objects under study is called population. The idea of sampling is to select a portion of the larger population...
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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使用统计和机器学习方法绘制风险运输基础设施资产的地图.

Rakesh Salunke1, Sadik Khan2

  • 1Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA. rakesh.salunke@jsums.edu.

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|January 29, 2026
PubMed
概括

绘制脆弱的高速公路堤和斜坡 (HWS) 对于交通基础设施至关重要. 这项研究开发了一种机器学习方法,以识别有风险的HWS,改善资产管理和防止山体滑坡.

关键词:
地理信息系统 (GIS) 是一个地质技术资产管理公司高速公路的坡道坡道.基础设施 基础设施机器学习就是机器学习.随机的森林随机的森林易感性 易感性 易感性

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科学领域:

  • 地质技术工程地质技术工程
  • 运输基础设施管理 运输基础设施管理
  • 地理信息系统 (GIS) 是指地理信息系统.

背景情况:

  • 高速公路堤防和坡道 (HWS) 是重要的,但经常被忽视的运输资产.
  • 高温水库易受山体滑坡的影响,极端降雨事件加剧了这种情况.
  • 准确地绘制易受伤害的HWS对于有效的基础设施管理至关重要.

研究的目的:

  • 开发和评估机器学习模型,用于绘制有风险的高速公路堤和斜坡的地图.
  • 为积极的资产管理创建可靠的易受伤害的HWS库存.
  • 确定影响HWS故障的关键因素.

主要方法:

  • 利用远程传感数据的数字海拔模型 (DEM) 来得出因果因素.
  • 开发了包括随机森林在内的监督机器学习模型.
  • 使用地质技术,地形学和水文数据训练模型,以已知的HWS故障位置作为地面真相.
  • 使用AUC,F1得分和准确度指标评估模型性能.

主要成果:

  • 随机森林模型获得了完美的分数 (AUC,F1,精度=1.0).
  • 为了平衡预测准确度,确定了0.75的最佳概率值.
  • 影响HWS故障的关键因素被确定为高度,与溪流的距离,NDVI和降水.

结论:

  • 开发的基于GIS的机器学习方法有效地绘制了大面积的脆弱HWS.
  • 这种方法使得有针对性的干预和优化了基础设施维护的资金使用.
  • 运输机构可以采用这种方法来进行战略地质技术资产管理.