基于LSTM和变压器模型的组合,对岩弹排放和石油物质度的长期预测
Feng Jiang1, Qiang Li2, Guotao Sun3
1College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China; Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guiyang 550025, China; Guizhou Geological Mineral Exploration and Development Bureau, 114th Geological Brigade, Zunyi, 563000, China; Karst Water Resources and Environment Academician Workstation of Guizhou Province, Zunyi 563000, China.
Water research
|January 26, 2025
概括
结合LSTM和iTransformer的组合模型可以改善长期预测石灰岩弹排放和污染物水平. 这种增强的地下水管理工具有助于减轻石油物质等污染物的风险.
科学领域:
- 水文地质学 水文地质学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 卡斯特泉源监测对于地下水管理至关重要,但由于含水层的复杂性,预测排放和污染物水平很困难.
- 长期的每小时预测对于应急响应至关重要,但仍未得到充分研究.
- 像LSTM和iTransformer这样的现有模型在捕捉长期变化或避免过度装配方面存在局限性.
研究的目的:
- 开发和评估一个整体模型,准确的长期每小时预测石泉排放和污染物度.
- 结合长期短期记忆 (LSTM) 和iTransformer模型的优势,以提高预测准确度.
- 评估模型在预测石油物质度以减轻危险方面的有效性.
主要方法:
- 开发了一个使用LSTM和iTransformer作为基础模型的集合模型,并随机使用森林元模型.
- 利用了来自中国西安东春 (2106小时) 的每小时降水,春季排水和石油物质度数据.
- 在各种时间步骤和交付时间方面,将整体模型的性能与单个LSTM和iTransformer模型进行了比较.
主要成果:
- LSTM捕获了短期模式,但在长期依赖方面遇到了困难.
- iTransformer有效地识别了复杂的模式,但显示出过度拟合的倾向.
- 整体模型表现出卓越的性能,平衡稳定性和灵敏性,在所有测试的时间步骤和交付时间中表现优于单个模型.
- 整体模型实现了强大的预测,带来了长时间 (6-10小时) 的预测.
结论:
- 开发的整体模型显著提高了长期每小时的岩弹排放和污染物度预测的准确性.
- 这种方法有效地减轻了与污染物相关的风险,如石油物质泄漏.
- 该研究强调了集体深度学习模型对复杂的地下水资源管理挑战的潜力.
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