通过使用改进的人工智能,通过每日溶解氧度预测水质
1University of Cambridge, Cambridge, CB2 1TN, UK. jy484@cam.ac.uk.
Scientific reports
|November 21, 2023
概括
这项研究引入了四种新方法来预测溶解氧 (DO) 水位,这是水质的关键指标. 电磁场优化-MLPNN模型被证明是DO预测中最有效和最准确的.
科学领域:
- 水文学的水文学
- 环境科学 环境科学
- 计算智能是一种计算智能.
背景情况:
- 溶解氧 (DO) 是一个关键的水文参数,也是水质的主要指标.
- 准确的DO预测对于有效的水资源管理和生态健康监测至关重要.
研究的目的:
- 引入和评估四种新的整合方法来预测DO度.
- 使用现实世界水文数据来比较这些方法的性能.
主要方法:
- 采用多层感知神经网络 (MLPNN) 作为预测模型.
- 使用了四种优化算法:基于教学学习的优化 (TLBO),正弦共弦算法,水循环算法 (WCA) 和电磁场优化 (EFO).
- 训练和测试模型使用来自俄勒冈州克拉马斯河 (USGS站) 的水文数据.
主要成果:
- 所有模型在DO预测中都显示出可靠性,WCA-MLPNN在训练阶段显示出初始优势 (MAE:0.9624).
- 在测试阶段,EFO-MLPNN和TLBO-MLPNN的表现略高于WCA-MLPNN,这是皮尔森相关系数 (Rp) 和根平均平方误差 (RMSE) 所示的.
- 鉴于复杂性和优化时间,EFO-MLPNN被确定为最有效的工具.
结论:
- 开发的整合模型,特别是EFO-MLPNN,为基于机器学习的DO建模提供了更高的准确性.
- 这些新的方法为水文参数预测和水质评估提供了先进的工具.
- 这些发现表明,环境监测的计算智能应用有着显著的进步.
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