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

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

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

278
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...
278
Quality of Water01:19

Quality of Water

498
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...
498
Testing Water Quality01:14

Testing Water Quality

341
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...
341
Typical Model Studies01:30

Typical Model Studies

613
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.
613
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

444
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...
444
Manipulation and Analysis01:21

Manipulation and Analysis

282
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...
282

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

LXNet: A lightweight CNN for lung disease classification from Chest X-ray with XAI-based interpretability.

PloS one·2026
Same author

Deceleration-phase restrengthening in compositionally zoned fault materials under transient slip.

Scientific reports·2026
Same author

Impact of Patient Demographics and Cardiovascular Risk Factors on Percutaneous Coronary Intervention Outcomes.

Cureus·2026
Same author

On-device artificial intelligence agent based on language models for electrochemical water desalination.

Water research·2026
Same author

Integrated machine learning and geochemical modeling reveal hydrogeochemical controls on fluoride and arsenic co-contamination in groundwater.

Environmental geochemistry and health·2026
Same author

Integrated approach for arsenic prediction and health risk evaluation in community tube wells installed by public health department: comparative study of random forest, extreme gradient boosting, and deep neural networks.

Environmental geochemistry and health·2026
Same journal

Near-bank vegetation patches reorganize hyporheic exchange pathways and spatiotemporal organization in near-bank zones.

Journal of environmental management·2026
Same journal

Decadal restructuring of driving forces for macroinvertebrate communities in China's Greater Bay Area.

Journal of environmental management·2026
Same journal

Global urban impacts of future climate extremes: Projections of heatwaves, droughts, and floods.

Journal of environmental management·2026
Same journal

A multi-method framework for unveiling nonlinear and interactive drivers of vegetation restoration: a case study in the South China Karst.

Journal of environmental management·2026
Same journal

Ecosystem functions and network complexity do not increase linearly with restoration levels on China's loess plateau.

Journal of environmental management·2026
Same journal

Preserving bare mudflats reduces methane emissions: Implications for coastal wetland management.

Journal of environmental management·2026
查看所有相关文章

相关实验视频

Updated: Jan 13, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K

一个基于图形的机器学习框架,用于在数据限制下管理河水质量.

Sueryun Choi1, Zahid Ullah2, Moon Son2

  • 1Gyeonggi-do Institute of Health and Environment Research, Cheongsa-ro 1beon-gil, Uijeongbu-si, Gyeonggi-do, 11780, Republic of Korea.

Journal of environmental management
|January 11, 2026
PubMed
概括
此摘要是机器生成的。

使用集成图形神经网络和可解释AI的机器学习框架改进了准确的河水质量预测. 这种方法有效地识别污染源,并指导数据有限环境中的管理策略.

关键词:
反事实的分析反事实的分析.可解释的人工智能 (XAI)图形神经网络 (GNN) 是一个神经网络.河流流域管理的管理.稀疏采样数据的稀疏采样数据.预测水质水质预测

更多相关视频

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

11.8K
Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management
05:04

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Published on: July 14, 2023

714

相关实验视频

Last Updated: Jan 13, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K
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

11.8K
Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management
05:04

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Published on: July 14, 2023

714

科学领域:

  • 环境科学 环境科学
  • 水资源管理 水资源管理
  • 机器学习应用 机器学习应用

背景情况:

  • 精确的河流水质预测受到稀疏数据和有限的流量信息的挑战,这在资源有限的流域监测中很常见.
  • 现有的方法往往难以有效地整合各种水环境变量以进行可靠的预测.

研究的目的:

  • 开发和验证用于河水质量预测,解释和管理的新型三模块机器学习框架.
  • 将这个框架应用于汉丹河流域的色度预测,解决数据限制.

主要方法:

  • 一个由三个模块组成的框架,将图形神经网络 (GNN) 或循环网络用于预测,可解释的AI用于解释,和反事实分析用于管理.
  • 利用来自59个地点的1667个月观测数据集,涵盖37个水环境变量.
  • 雇员独立培训,验证和测试套件用于严格的绩效评估.

主要成果:

  • 基于图形的模型,特别是增强的图形样本和汇总模型,表现优于经常性基线,达到0.82.2的测试R2.
  • 解释性分析确定了SC分流域作为主要干预区域,并区分了长期和短期的污染驱动因素.
  • 反事实分析表明可行的下游色度目标 (14-15 CU),成功率为26-40%.

结论:

  • 拟议的机器学习框架显著提高了河水质量预测的准确性和可解释性.
  • 它为流域管理提供了成本效益高的决策支持工具,特别是在数据有限的条件下.
  • 该研究强调了GNN在捕获污染源特征和运输途径方面的有效性.