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基于灵敏度的双向机器学习辅助的随机搜索方法用于地下水DNAPL源表征.

Zeyu Hou1,2, Yingzi Lin3,4, Tongzhe Liu5,6

  • 1Key Laboratory of Songliao Aquatic Environment, Ministry of Education, Jilin Jianzhu University, Changchun, 130118, China. houzeyu890829@163.com.

Environmental science and pollution research international
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概括

本研究引入了一种机器学习方法,用于识别地下水污染源和运输参数. 与传统方法相比,这种方法提高了准确性和效率,提供了更精确的污染源信息.

关键词:
双向的模式识别系统.地下水污染 污染 地下水污染机器学习是机器学习.基于灵敏度的随机搜索源代码的特征描述

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科学领域:

  • 环境科学 环境科学
  • 机器学习 机器学习
  • 水文地质学 水文地质学

背景情况:

  • 密度非水相液体 (DNAPL) 污染在地下水整治方面带来了重大挑战.
  • 准确识别DNAPL源特性和污染物运输参数对于有效的现场管理至关重要.
  • 现有的方法通常在复杂的地下环境中难以满足计算需求和准确性.

研究的目的:

  • 为DNAPL源标识开发一种基于机器学习的有效并行全球搜索方法.
  • 提高污染物运输参数估计的准确性和减少不确定性.
  • 为传统的反转技术提供更强大,更高效的计算替代方案.

主要方法:

  • 设计了一个贝叶斯反转框架,结合了一个集群智能组织的混合内核极端学习机器 (SIO-HKELM).
  • 开发了一种适应式反向HKELM用于初始参数估计和预先信息校正.
  • 集成了一个基于灵敏度的 Metropolis 标准 (MC) 与动态粒子群优化 (SD-PSO) 进行了集成,以提高搜索 ergodicity.

主要成果:

  • 与KELM和SVR相比,SIO-HKELM表现出优越的概括性和稳定性,以高精度近似数值模型映射 (向前R2=0.9944,反向R2=0.6440).
  • 适应式反向-HKELM方法减少了反向不确定性,并在大约60次代内加速了向后分布的收.
  • SD-PSO-MC有效地覆盖了搜索空间,限制了"等效性",并将估计误差降低到<8%,优于多链马尔科夫链蒙特卡洛 (MCMC) 方法.

结论:

  • 拟议的基于机器学习的平行全球搜索方法为DNAPL源特性和污染物运输参数反转提供了精确有效的工具.
  • 与传统的MCMC方法相比,SD-PSO-MC方法提供了更准确的真实来源信息.
  • 这项研究推动了先进的机器学习技术在水文地质污染物调查中的应用.