相关实验视频
Updated: Jul 24, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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一个基于证据理论的嵌入模型,用于管理智能水环境
Maha Driss1,2, Wadii Boulila2,3, Haithem Mezni4,5
1Security Engineering Lab, CCIS, Prince Sultan University, Riyadh 12435, Saudi Arabia.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究引入了一种新的方法,通过将物联网 (IoT) 数据与先进的学习技术集成,来管理水资源. 它解决了数据的不确定性,以便做出更好的水资源管理决策.
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:
- 水资源稀缺和质量下降对人类福祉和可持续发展构成重大威胁.
- 基于物联网 (IoT) 的连续水测量对于环境监测至关重要,但存在不确定性问题.
- 感知水数据的不确定性可能导致有偏见的分析和错误的决策.
研究的目的:
- 通过解决基于物联网的水测量的不确定性,开发一种强大的水资源管理方法.
- 提高水信息系统的准确性和可靠性,以便有效地做出决策.
- 提出一个框架,将网络代表学习和水资源管理的不确定性处理结合起来.
主要方法:
- 利用网络表示学习来建模水信息系统.
- 结合概率技术来解释感知水数据中的不确定性.
- 应用证据理论用于意识到不确定性的决策和战略选择.
主要成果:
- 开发水网的概率嵌入的开发.
- 不确定的水信息实体的分类.
- 促进水资源管理战略的知情决策.
结论:
- 结合网络表示学习和不确定性处理,为水资源管理提供了严格而高效的方法.
- 提出的方法提高了水数据分析和决策过程的可靠性.
- 面对日益稀缺和数据不确定性的情况,这种方法对于可持续的水资源管理至关重要.
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