基于数值模拟和机器学习的地下水循环井设计优化
Zhang Fang1, Hao Ke2, Yanling Ma2
1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, People's Republic of China. azhang9456@126.com.
Scientific reports
|May 20, 2024
概括
这项研究引入了机器学习,用于最优的地下水循环井 (GCW) 设计,比传统方法提高速度和范围. 该方法有效地优化GCW参数,以加强地下水整治.
科学领域:
- 环境工程 环境工程
- 水文地质学 水文地质学
- 机器学习应用 机器学习应用
背景情况:
- 地下水循环井 (GCW) 的最佳设计对于有效的地下水整治至关重要,但在传统的模拟方法中面临着挑战.
- 传统方法的局限性包括漫长的建模时间,随机优化和不完整的结果.
研究的目的:
- 利用机器学习 (ML) 开发一种创新和高效的方法,以优化GCW的设计.
- 与传统方法相比,提高GCW设计参数优化的速度和范围.
主要方法:
- 使用FloPy包创建MODFLOW和MODPATH模型用于GCW模拟.
- 计算的关键性能指标:影响半径 (R) 和颗粒回收率 (Pr).
- 训练和评估ML模型 (MLR,ANN,SVM) 使用3000个运营效率指标的数据集.
主要成果:
- 开发了ML模型,展示了预测结果和经验数据之间的强烈相关性.
- 在西安站点实现了影响半径 (R) 和粒子回收率 (Pr) 的优化GCW参数.
- 证明了ML显著加快了优化并扩大了参数搜索空间.
结论:
- 数字模拟与机器学习的集成为优化GCW设计和预测修复结果提供了有效的策略.
- 这种混合方法克服了传统方法的局限性,为地下水净化提供了更全面,更有效的解决方案.
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