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Watershed Planning within a Quantitative Scenario Analysis Framework
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使用深度学习和加权平均组合模型预测水质变量.

Mohammad G Zamani1, Mohammad Reza Nikoo2, Sina Jahanshahi3

  • 1Department of Water Resources Engineering, Faculty of Civil, Water and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran.

Environmental science and pollution research international
|November 23, 2023
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概括
此摘要是机器生成的。

这项研究使用深度学习模型预测水生生态系统中的叶绿素a (Chl-a) 度. 集成模型集成基因算法,特别是NSGA-II,显著提高了对水质评估的预测准确性.

关键词:
深度学习 (DL) 是指深度学习.组合模型模型组合模型非主导的遗传算法 (NSGA-II)单一和多目标优化算法预测水质 预测水质

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科学领域:

  • 环境科学 环境科学
  • 水质监测 水质监测
  • 计算生态学计算生态学

背景情况:

  • 叶绿素a (Chl-a) 是水生生态系统健康的关键指标,反映了藻类和菌种群.
  • 准确的Chl-a预测对于有效的水质管理和生态评估至关重要.

研究的目的:

  • 评估四种深度学习 (DL) 模型的预测性能:循环神经网络 (RNN),长期短期记忆 (LSTM),封闭循环单元 (GRU) 和时间卷积网络 (TCN) 用于Chl-a预测.
  • 通过整合使用遗传算法 (GA) 和非主导排序遗传算法II (NSGA-II) 的DL模型来开发集合模型 (EM),以提高预测准确度.
  • 评估开发的EMs在预测Chl-a度方面的有效性.

主要方法:

  • 使用每小时的Chl-a度数据,延迟时间长达6小时,作为预测Chl-a (t+1) 的输入.
  • 经过训练和验证的DL和EM模型分别对70%和30%的数据集进行了训练和验证,这些数据集来自希腊小普雷斯帕湖 (Small Prespa Lake).
  • 使用单一目标GA和多目标NSGA-II来开发组合模型.

主要成果:

  • 在独立的DL模型中,GRU模型表现出优越的性能,超过了RNN,LSTM和TCN.
  • 使用GA和NSGA-II开发的组合模型有效地预测了低度和高度的Chl-a.
  • EM-NSGA-II模型实现了最高的准确性,在各种评估指标 (包括R平方) 上显著优于独立的DL模型和EM-GA.

结论:

  • 深度学习模型,特别是GRU,显示出对Chl-a预测的前景.
  • 使用NSGA-II组合建模为改善水生生态系统中Chl-a预测精度提供了强大的方法.
  • 开发的EM-NSGA-II为水质监测和管理提供了一个非常有效的工具.