一个溶解氧预测模型,集成ChatGPT专家知识驱动的注意力机制和正常化
Xiangfeng Bu1, Li Wang1, Xiaoyi Wang2
1School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China; Beijing Laboratory for Intelligent Environmental Protection, Beijing 100048, China.
Journal of contaminant hydrology
|September 16, 2025
概括
这项研究引入了ChatGPT-EK-TabNet,这是一种用于预测溶解氧 (DO) 度的新型模型. 通过整合领域知识,它显著提高了对地表水质量管理的预测准确性.
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 环境管理中的人工智能
背景情况:
- 溶解氧 (DO) 对于评估地表水的健康至关重要.
- 现有的DO预测模型缺乏领域知识,导致准确度低于最佳.
- 历史数据中的异常值和分布差异阻碍了可靠的预测.
研究的目的:
- 通过结合水质领域知识,开发一个准确的DO预测模型.
- 为了减轻异常值和盆地间差异对DO度预测的影响.
- 增强特征相互作用分析,以实现更可靠的水质建模.
主要方法:
- 提出了一种改进的规范化方法,使用领域专家知识来处理异常值.
- 开发了一个加权的特征约束矩阵,用于注意力机制的领域知识.
- 改进了TabNet模型,用于DO预测中的稀疏适应特征选择.
主要成果:
- 在米云水库数据上,ChatGPT-EK-TabNet模型实现了高精度 (RMSE=0.3349,MAE=0.2101,R2=0.9388).
- 拟议的模型优于传统方法,将RMSE降低了0.1222%,MAE降低了0.2187.
- 在多个数据集 (小兴海湖,乌苏里河,穆林河) 中展示了出色的概括性.
结论:
- 来自ChatGPT的域名知识提高了DO预测的准确性,超出了数据驱动的方法.
- 该模型提供细粒度表示和跨域概括能力.
- 这种方法支持可持续的地表水管理,可以应用于其他水质指标.
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