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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Survival Tree01:19

Survival Tree

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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.
 Building a Survival Tree
Constructing a...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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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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Manipulation and Analysis01:21

Manipulation and Analysis

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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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Response Surface Methodology01:16

Response Surface Methodology

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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.
The process of RSM involves several key steps:
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Watershed Planning within a Quantitative Scenario Analysis Framework
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通过在线协作战略促进森林景观动态预测.

Zaiyang Ma1, Chunyan Wu2, Min Chen1

  • 1Key Laboratory of Virtual Geographic Environment (Ministry of Education of PR China), Nanjing Normal University, Nanjing, Jiangsu, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing, Jiangsu, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing, Jiangsu, China.

Journal of environmental management
|January 18, 2024
PubMed
概括

本研究介绍了合作森林景观建模的在线策略. 它增强了数据准备,场景配置和任务安排,以更好地预测森林动态和决策.

关键词:
合作框架 合作框架森林景观建模 森林景观建模兰迪斯二世 兰迪斯二世开放GMS是一个开放的GMS.基于网络的预测预测.

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科学领域:

  • 林业和环境科学 林业和环境科学
  • 计算生态学计算生态学
  • 地理空间分析的研究.

背景情况:

  • 森林景观动态对于管理和政策至关重要,特别是在气候变化和干扰的情况下.
  • 预测这些动态需要专家的合作,但在去中心化数据和离线计算方面存在挑战.
  • 现有的网络工具有助于一些协作,但核心建模任务仍然存在差距.

研究的目的:

  • 提出和演示森林景观动态预测的在线协作策略.
  • 克服分散数据,离线计算和协作建模中的复杂场景的挑战.
  • 支持参与式建模过程和森林管理中的决策.

主要方法:

  • 开发了一个由四个模块组成的在线协作策略:数据准备,模型计算,场景配置和流程组织.
  • 启用了自愿数据收集,在线处理和森林景观模型 (FLM) 的同步使用.
  • 通过使用LANDIS-II模型,促进了模拟场景的协作设计,修改和执行.

主要成果:

  • 在线策略有效地改善了森林景观动态预测.
  • 在数据准备,场景配置和任务安排方面观察到显著的进步.
  • 该方法成功支持了森林地表生物质动态的预测.

结论:

  • 拟议的在线协作战略增强了森林景观动态预测能力.
  • 它解决了协作建模的关键挑战,促进了高效和参与的流程.
  • 这种方法为森林相关的决策和政策制定提供了宝贵的支持.