根据反向传播神经网络替代模型和灰狼优化算法在不确定性条件下的地下水污染监测网络的最佳设计
Xinze Guo1,2,3, Jiannan Luo4,5,6, Wenxi Lu1,2,3
1Key Laboratory of Groundwater Resources and Environment (Jilin University), Ministry of Education, Jilin University, Changchun, 130021, China.
Environmental monitoring and assessment
|January 10, 2024
概括
这项研究通过使用模拟优化和蒙特卡洛方法来优化地下水污染监测网络,以处理不确定性. 替代模型和灰狼优化器提高了计算效率和准确性,以更好地跟踪污染羽毛.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 计算建模 计算建模
背景情况:
- 地下水污染监测网络 (GPMN) 的设计受到模拟模型不确定性的挑战,影响了可靠性.
- 污染源强度和液压导电性的不确定性是影响GPMN设计的关键因素.
- 现有的方法经常面临计算负担,并且可能陷入局部最佳状态.
研究的目的:
- 在不确定性条件下开发最佳的地下水污染监测网络设计.
- 通过解决源和液压不确定性,提高污染监测的可靠性和准确性.
- 在设计过程中减少计算负载并避免局部最佳值.
主要方法:
- 利用模拟优化和蒙特卡洛方法来确定最佳的监测井布局.
- 采用反向传播神经网络 (BPNN) 作为替代模型来减少计算需求.
- 整合了灰狼优化器 (GWO) 算法,以克服局部最佳并改善勘探.
- 应用空间时刻方法来评估污染雾监测的准确性.
主要成果:
- 替代BPNN模型有效地近似了模拟模型的输入输出关系,大大减少了计算.
- GWO算法成功解决了优化模型,提高了解决方案的准确性,避免了局部最佳.
- 优化的GPMN准确地描述了每年监测期间的污染羽流分布.
- 综合方法有效地解决了不确定性下的最佳GPMN设计问题.
结论:
- 结合模拟优化,蒙特卡洛方法,BPNN替代模型和GWO,为强大的GPMN设计提供了有效的解决方案.
- 拟议的方法提高了复杂的环境监测挑战的计算效率和解决方案准确性.
- 优化的网络确保可靠和准确的特征动态污染羽毛迁移随时间的推移.
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