Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Testing Water Quality01:14

Testing Water Quality

187
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...
187
Quality of Water01:19

Quality of Water

191
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...
191

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Hereditary spastic paraplegia in three siblings with distinct genetic mutations.

The Journal of international medical research·2026
Same author

Functional differentiation of glomalin-related soil protein fractions reveals dual pathways for carbon storage in organic farming systems.

The Science of the total environment·2026
Same author

Microbial efficiency enhancement drives carbon sequestration in long-term organic farming systems: linking taxonomic succession to carbon use efficiency.

Frontiers in microbiology·2026
Same author

Strong Metal-Support Interaction in Metal-Organic Framework (MOF)-Derived Multiphase TiO<sub>2</sub> Supports for Durable Pt Catalysts in Acidic Hydrogen Evolution.

ACS applied materials & interfaces·2026
Same author

Artificial intelligence based retinal imaging for cardiovascular risk and statin guidance in retinal vein occlusion.

American journal of preventive cardiology·2026
Same author

A Drought-Activated Bacterial Symbiont Enhances Legume Resilience Through Coordinated Amino Acid Metabolism.

Microorganisms·2026

相关实验视频

Updated: Sep 13, 2025

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.3K

一个基于机器学习的自动化框架,用于用传感器数据预测地下水质量.

Jaeuk Youn1, Do Hwan Jeong2, MoonSu Kim2

  • 1Department of Environmental & Energy Engineering, Yonsei University, Wonju, 26493, Republic of Korea.

Journal of environmental management
|July 27, 2025
PubMed
概括

一个自动化框架使用传感器数据和机器学习准确预测地下水中的氨 (NH3-N). 这种方法增强了地下水质量监测和污染检测.

关键词:
在AutoML中使用AutoML.地下水的地下水.机器学习是机器学习.实时预测 实时预测传感器数据校准的校准

更多相关视频

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.4K

相关实验视频

Last Updated: Sep 13, 2025

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds
06:37

Continuous Hydrologic and Water Quality Monitoring of Vernal Ponds

Published on: November 13, 2017

9.3K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.1K
Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
12:50

Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds

Published on: September 26, 2017

11.4K

科学领域:

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

背景情况:

  • 地下水质量监测对于有效的地下水管理至关重要.
  • 实时和准确的测量技术对于及时干预至关重要.
  • 现有的方法可能缺乏持续监控所需的精度和速度.

研究的目的:

  • 开发一个用于预测地下水中的氨 (NH3-N) 的自动化框架.
  • 利用多参数传感器数据和机器学习来提高准确性.
  • 提高地下水质量监测的效率和可靠性.

主要方法:

  • 从尸体埋葬地点收集传感器数据,随后进行严格的质量控制和实验室校准.
  • 应用自动机器学习 (AutoML) 用关键功能优化NH3-N预测模型.
  • 在各种水文地质数据集上验证了框架的预测性能.

主要成果:

  • 优化模型显著提高了预测准确性:R2从0.76增加到0.90.
  • 错误指标大幅下降:RMSE从0.84降至0.38,MAE从0.57降至0.23.
  • 外部验证证实了不同地区的强表现 (R2 = 0.89-0.98).

结论:

  • 拟议的框架有效地将校准的传感器数据与可靠的地下水监测的自动化模型选择相结合.
  • 这种方法提供了一个可扩展的解决方案,用于在敏感环境中早期检测污染.
  • 先进的分析和自动校准增强了污染警报,支持积极的地下水管理.