一个集成的建模框架,用于在地表水中识别多重污染源
Xiaodong Liu1, Xuneng Tong2, Lei Wu3
1Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China; Yangtze Institute for Conservation and Development, Hohai University, Jiangsu 210098, China.
Journal of environmental management
|October 1, 2023
概括
这项研究引入了使用人工神经网络和粒子群优化的混合模型,用于准确识别水体中的污染源. 开发的框架有效地确定了污染源,并量化了排放强度,加强了水安全管理.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 计算建模 计算建模
背景情况:
- 识别污染源对于有效的水安全管理至关重要.
- 现有的方法可能缺乏复杂水系统所需的速度,准确性或可靠性.
- 综合建模框架为应对这些挑战提供了一个有希望的方法.
研究的目的:
- 开发和评估一个集成的模拟优化建模框架,以快速,准确和可靠地识别污染源.
- 测试框架在稳定 (流水) 和不稳定 (长江河口) 流量条件下的有效性.
- 在框架内比较不同人工神经网络 (ANN) 算法的性能.
主要方法:
- 开发基于过程的模型 (PBM) 来模拟水力动力学和水质.
- 使用PBM生成的数据创建了人工神经网络 (ANN) 替代模型 (BP,RBF,GRNN).
- 将PBM-ANNs与粒子群优化 (PSO) 结合起来,形成一个混合建模框架.
- 使用实验室流水和现实生活河口数据验证了框架.
主要成果:
- 混合PBM-ANNs-PSO模型成功地确定了污染源,并量化了连续释放点源的排放强度.
- 使用多个标准 (R2,RMSE,MAE) 评估模型性能为"优秀预测".
- 与PSO (BP-PSO) 相结合的反向传播-ANNs表现出优异的性能,相对误差通常低于5%.
结论:
- 综合模拟优化框架为识别污染源和量化污染强度提供了强大的工具.
- BP-PSO模型是水系统中污染源识别的高效方法.
- 调查结果为地方政府机构在做出明智的污染控制决策时提供了宝贵的见解.
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