使用集群智能基于模拟优化模型识别地下水污染源
K Swetha1, T I Eldho2, L Guneshwor Singh3
1Homi Bhabha National Institute (HBNI), Mumbai, India.
Environmental science and pollution research international
|December 31, 2024
概括
本研究引入了一个链接模拟优化模型,用于精确确定地下水污染源. 与LRPIM-PSO和LRPIM-GWO相比,LRPIM-TLBO模型在识别污染源和释放历史方面表现出更高的准确性.
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:
- 地下水污染对环境和健康构成重大风险.
- 准确识别污染源对于有效的整治策略至关重要.
- 现有的来源识别方法经常面临复杂的含水层系统的挑战.
研究的目的:
- 开发和评估用于地下水污染源识别 (SI) 的链接模拟优化 (SO) 模型.
- 将无网格局部辐射点间接方法 (LRPIM) 与群集智能优化算法集成.
- 为了比较SI的LRPIM-TLBO,LRPIM-PSO和LRPIM-GWO的性能.
主要方法:
- 使用LRPIM开发了一个基于向-分散-反应方程 (ADRE) 的模拟模型.
- 该LRPIM模拟模型与基于教学学习的优化 (TLBO),灰狼优化 (GWO) 和粒子优化 (PSO) 相结合.
- 该SO模型应用于假设和实际的含水层问题,以确定污染源位置和释放历史,从而最大限度地减少预测观察差异.
主要成果:
- 所有三个开发的SO模型 (LRPIM-TLBO,LRPIM-PSO,LRPIM-GWO) 都成功识别了地下水污染源及其释放历史.
- 在源标识方面,LRPIM-TLBO模型表现出最高的准确性.
- LRPIM-PSO和LRPIM-GWO也提供了令人满意的结果,其中LRPIM-PSO比LRPIM-GWO更准确.
结论:
- 链接模拟优化模型是识别地下水污染源的有效工具.
- LRPIM-TLBO方法提供了一个非常准确的方法来确定污染物源参数.
- 这项研究强调了小群智能算法在解决复杂的水文地质挑战方面的潜力.
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