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相关概念视频

Testing Water Quality01:14

Testing Water Quality

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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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Quality of Water01:19

Quality of Water

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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Multiple Regression01:25

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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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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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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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相关实验视频

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基于机器学习和综合权衡方法的水质预测.

Xianhe Wang1,2, Ying Li1,2, Qian Qiao1

  • 1School of Applied Chemistry and Materials, Zhuhai College of Science and Technology, Zhuhai 519041, China.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
概括

准确的水质预测对于环境保护至关重要. 本研究引入了一种新的特征选择方法,并评估机器学习模型,发现长短期记忆 (LSTM) 网络对于时间序列水质预测非常有效.

关键词:
这是LSTM的LSTM.这是一个基于权重的综合方法.功能选择 功能选择机器学习是机器学习.预测水质水质预测

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科学领域:

  • 环境科学 环境科学
  • 数据科学数据科学数据科学
  • 水资源管理 水资源管理

背景情况:

  • 全球环境问题需要强有力的水资源保护和生态平衡.
  • 准确的水质监测和预测对于环境保护至关重要.
  • 现有的水质预测方法在准确性和可靠性方面面临挑战.

研究的目的:

  • 开发一种全面的基于重量的方法来选择水质预测中的关键特征.
  • 评估各种机器学习模型的性能,以预测水质.
  • 确定最有效的模型,以准确和可靠地预测水质.

主要方法:

  • 开发了一种结合权和皮尔森相关系数的新型特征选择方法.
  • 探索了多种机器学习模型,包括支持矢量机器 (SVM),多层感知器 (MLP),随机森林 (RF),XGBoost和长短期内存 (LSTM).
  • 基于各种水质参数的预测准确性和稳定性来评估模型性能.

主要成果:

  • 综合权重方法有效地根据相关性和信息内容选择了特征,减少了偏差.
  • 支持向量机 (SVM) 在预测溶解氧 (DO) 方面表现强.
  • 多层感知器 (MLP) 在多个水质参数的非线性建模方面表现出色.
  • 随机森林 (RF) 和XGBoost的表现相对较低.
  • 长期短期记忆 (LSTM) 网络在捕获时间序列水质预测的动态模式方面表现出卓越的准确性和稳定性.

结论:

  • 拟议的综合特征选择方法提高了水质预测的准确性和稳定性.
  • 长短期记忆 (LSTM) 网络对于时间序列水质预测非常有效,因为它们能够捕获动态模式.
  • 机器学习模型为改善水质监测和管理提供了有希望的解决方案.