同时识别地下水污染源和液压参数基于多层感知器和飞优化飞的同时识别
Yidan Li1,2,3, Wenxi Lu4,5,6, Zidong Pan1,2,3
1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun, 130021, China.
概括
本研究介绍了新的飞优化 (FFO) 算法,用于准确地识别地下水污染源 (GCSI). FFO显著优于传统方法,改善了整治工作.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 计算机建模 计算建模
背景情况:
- 地下水污染源识别 (GCSI) 对于环境保护和责任评估至关重要.
- 传统的GCSI模拟优化方法面临着高维变量和局部最佳值的挑战,降低了准确性.
- 现有的启发式算法可能无法为复杂的地下水污染问题找到准确的解决方案.
研究的目的:
- 提出一种新的优化算法,即飞狐优化 (FFO),用于精确地识别地下水污染源.
- 评估FFO在同时识别污染源释放历史和液压导电性的有效性.
- 开发和评估使用多层感知 (MLP) 来减少计算负载的替代模型.
主要方法:
- 开发并应用飞优化 (FFO) 算法来解决GCSI优化模型.
- 执行了地下水污染源释放历史和液压导电性的同时识别.
- 利用多层感知 (MLP) 创建模拟模型的替代模型,与反向传播 (BP) 算法进行比较.
主要成果:
- FFO算法实现了2.12%的平均相对误差,显著超过了基因算法 (GA).
- 基于MLP的替代模型显示了超过0.999的适配精度,证明它优于BP替代模型.
- FFO有效地解决了GCSI中的非线性和高维变量挑战.
结论:
- 飞狐优化 (FFO) 算法为地下水污染源识别提供了更准确,更有效的解决方案.
- 基于MLP的替代模型可以在GCSI模拟中大幅降低计算成本.
- 这项研究提供了一个强大的框架,通过先进的计算方法来改进地下水整治策略.
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