使用数据驱动的变压器模型识别时间变化的污染物排放率
Yaning Xu1, Wenxi Lu1, Liuzhi Zhu1
1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China.
Journal of contaminant hydrology
|January 13, 2026
概括
变压器模型通过并行处理数据来增强地下水污染源的识别,比传统方法提高了准确性. 这种人工智能方法对于有效保护地下水资源至关重要.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 人工智能的人工智能
背景情况:
- 识别地下水污染源 (IGCS) 对于补救和资源保护至关重要.
- 循环神经网络 (RNN) 由于逐步处理数据而面临准确性和效率的限制.
研究的目的:
- 应用变压器模型直接识别地下水污染源特征.
- 通过利用变压器的自我注意机制来克服RNN的局限性,用于并行处理和远程依赖性捕获.
主要方法:
- 利用了变压器模型,一个具有自我注意力机制的深度学习架构.
- 进行了假设的案例研究,比较了变压器,MLP反向映射,优化和数据同化方法.
主要成果:
- 变压器模型在识别污染源特征方面表现出卓越的准确性.
- 数据同化提供了令人满意的结果与较低的计算成本,适合时间敏感的应用程序.
- 优化方法在计算上是密集的,并且比其他方法更不准确.
结论:
- 变压器模型为准确的IGCS带来了重大进步.
- 数据同化对于时间有限的场景来说是一个可行的替代方案.
- 人工智能驱动的方法为保护地下水资源提供了更好的性能.
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