相关实验视频
Updated: Jun 24, 2025

12:44
Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
8.0K
基于物流回归,极端梯度提升和随机森林建模方法预测洪水敏感性
Ying Wu1, Zhiming Zhang2, Xiaotian Qi1
1Department of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, No. 1 Zhanlanguan Road, Beijing 100044, China.
概括
随机森林模型在评估洪水敏感性方面表现出色,识别了高风险地区,覆盖了苏市的44%. 这项研究有助于了解洪水模式,并制定有效的风险管理策略.
科学领域:
- 环境科学 环境科学
- 地理空间分析是什么
- 机器学习应用 机器学习应用
背景情况:
- 洪水每年对生命和财产构成重大全球威胁.
- 有效的洪水敏感性评估对于减少灾害风险至关重要.
研究的目的:
- 评估和确定用于洪水敏感性评估的表现最佳的机器学习模型.
- 分析影响洪水敏感性的关键因素及其空间模式.
- 为了生成苏市的洪水敏感度地图.
主要方法:
- 使用了三个机器学习模型:物流回归 (LR),极端梯度提升 (XGBoost) 和随机森林 (RF).
- 采用了15个气象,水文和地理空间变量,其中12个是在多对线性分析后选择的.
- 用苏市的历史洪水点构建了一个数据集,用于模型培训和验证.
主要成果:
- 随机森林 (RF) 模型表现出卓越的性能,具有最高的AUC值,准确性和整体有效性.
- 创建了一个洪水敏感度地图,将地区分为五个级别,从非常低到非常高的敏感度.
- 大约44%的研究区域被确定为高风险地区,主要位于老城的中部,东部和南部地区.
结论:
- 随机森林模型是洪水风险评估的可靠和有效工具.
- 生成的洪水敏感度地图为有针对性的洪水管理和城市规划提供了宝贵的见解.
- 了解洪水风险的空间分布对于减轻未来洪水影响至关重要.
相关概念视频
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
43
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...
43
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
Survival Tree
79
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.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
79
Sensitivity, Specificity, and Predicted Value
284
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
284
Steps in Outbreak Investigation
122
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
122
Residuals and Least-Squares Property
7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K

