通过将混合深度学习模型应用于真实案例数据,研究预测有毒重气体二氧化硫的扩散情况
Yuchen Wang1, Zhengshan Luo1, Jihao Luo2
1School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, China.
The Science of the total environment
|August 24, 2023
概括
预测二氧化硫 (SO2) 扩散对于环境安全至关重要. 一个新的混合深度学习模型PC CNN-GRU提供了更快,更准确的SO2度预测,有助于紧急决策.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 二氧化硫 (SO2) 具有重大环境和安全风险.
- 传统的SO2扩散模型缺乏应急响应所需的精度和速度.
- 深度学习 (DL) 为准确和快速的气体度预测提供了一个有希望的替代方案.
研究的目的:
- 开发一种创新的混合DL模型来预测SO2扩散.
- 使用生成模型解决SO2扩散研究中的数据稀缺问题.
- 量化SO2扩散的关键驱动因素,并可视化安全下风距离.
主要方法:
- 开发了一个并行连接的卷积神经网络通道循环单元 (PC CNN-GRU) 模型.
- 使用时间序列生成对抗网络 (TimeGAN) 来增强有限的SO2扩散数据.
- 转换重要性 (PIMP) 和Sobol'方法用于驱动器贡献分析.
主要成果:
- 电脑CNN-GRU模型在草原草 (PPG) 项目数据上获得了高精度 (RMSE: 20.1658,MAE: 10.9158,R2: 0.9288).
- 时间GAN有效地生成了用于SO2扩散研究的合成数据.
- 驾驶员的贡献被量化,并可视化了最大的安全下风距离.
结论:
- 电脑CNN-GRU模型为SO2扩散预测提供了精确有效的解决方案.
- 集成TimeGAN解决了环境建模中的数据限制.
- 这些发现为SO2风险评估和缓解策略提供了科学基础.
相关概念视频
01:18Steps in Outbreak Investigation
152
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
152
01:22The Sulfur Cycle
44.9K
Sulfur, an important element in the chemical makeup of proteins, is recycled through the atmosphere and aquatic and terrestrial environments. Found in the atmosphere as sulfur dioxide (SO2), sulfur is released by decaying organisms, weathered rocks, geothermal vents, volcanos, and burning fossil fuels. It is deposited into the ecosystem, cycled through the biotic community, and either released back into the atmosphere as gas or deposited in marine sediment for long-term storage and eventual...
44.9K