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基于混合深度学习的平原流域水质的预测

Kefan Wang¹, Lei Liu¹, Xuechen Ben²

¹College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, 310058, China.

Environmental research

|September 5, 2024

概括

深度学习模型,特别是混合贝叶斯-长期短期记忆门式反复单元 (LSTM-GRU) 模型,在复杂的河流网络中显著提高了水质预测的准确性. 特性选择和数据频率提高模型性能,以有效管理水环境.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
水文学的水文学

背景情况:

有效的水环境管理依赖于准确的水质预测模型.
平原流域呈现复杂的液压条件,挑战现有的预测模型.
机器学习的进步为改善水质预测提供了潜力.

研究的目的:

评估传统的机器学习与深度学习模型对平原河流网络的水质预测.
开发和评估一种新的混合深度学习模型,以提高预测准确度.
调查输入特征和数据时间频率对模型性能的影响.

主要方法:

三种传统机器学习模型与三种深度学习模型 (包括LSTM和GRU) 的比较.
开发一个混合贝叶斯-LSTM-GRU模型.
使用各种输入特征集和数据时间频率评估模型性能.
评估指标包括R平方和根平均平方误差 (RMSE).

主要成果:

深度学习模型显著超过了传统的机器学习模型.
长期短期记忆 (LSTM) 模型的R平方改善了约29%,RMSE减少了约48.6%.
与单独使用LSTM相比,混合贝叶斯-LSTM-GRU模型进一步将平均RMSE降低了18.1%.
选择特征数据集和更高的时间频率通常会产生更好的结果,除了突然变化.

结论:

混合深度学习模型提供了一种高效和具有成本效益的方法来提高水质预测.
开发的模型显示了在平原流域水质管理中实际应用的巨大潜力.
优化输入特征和时间数据分辨率对于可靠的水质预测至关重要.

关键词:

贝叶斯优化是贝叶斯的优化.有门的经常性单位.混合动力模型混合动力模型长期短期记忆短期记忆机器学习是机器学习.预测水质水质预测

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