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相关概念视频

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

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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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Modeling and Similitude01:12

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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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.
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Uncertainty: Overview00:59

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Watershed Planning within a Quantitative Scenario Analysis Framework
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基于遥感的机器学习用于在多维不确定性下进行河水质量建模.

Saiful Haque Rahat1, Todd Steissberg2, Won Chang3

  • 1Geosyntec Consultants, 920 SW 6th Ave Suite, 600, Portland, OR 97204, United States of America.

The Science of the total environment
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概括
此摘要是机器生成的。

这项研究使用机器学习和卫星数据来改进河水质量预测,克服稀疏地面数据的局限性和更好的环境监测的复杂影响因素.

关键词:
机器学习是机器学习.遥感是一种远程传感.总悬浮固体总量 悬浮固体总量水的质量 水的质量

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科学领域:

  • 环境科学 环境科学
  • 水文学的水文学
  • 遥感 遥感 遥感 遥感
  • 机器学习 机器学习

背景情况:

  • 河流水质量模拟受到数据和模型不足的阻碍,这些数据和模型无法捕捉复杂的影响因素.
  • 传统的总悬浮固体 (TSS) 数据收集很少,限制了对气候极端和水污染的分析.
  • 现有的模型与影响河水质量的因素的多维性斗争,包括水气候条件和土地利用.

研究的目的:

  • 开发一种技术,用遥感数据来增强有限的地面水质观测.
  • 利用机器学习来解释对河水质量的复杂,多维的影响.
  • 在气候不确定性下,改进对水质变量,如总悬浮固体 (TSS) 的预测.

主要方法:

  • 使用长期短期记忆网络 (LSTM) 模型.
  • 在中等分辨率成像光谱辐射仪 (MODIS) 卫星反射率数据上训练LSTM模型.
  • 通过俄俄河谷水卫生委员会 (ORSANCO) 的总悬浮固体 (TSS) 数据对模型进行校准.

主要成果:

  • 成功增强有限的地面水质数据与遥感反射率数据.
  • 开发了一种基于数据的算法,能够计算流域内的空间变化.
  • 在不确定性下证明有效的水质预测,使用TSS作为污染物的替代品.

结论:

  • 拟议的方法通过整合卫星数据和机器学习来增强水质模拟.
  • 这种方法解决了环境监测中的数据稀缺性和模型复杂性问题.
  • 开发的技术为经验分析和数据驱动的水质预测提供了一个强大的框架.