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结合基于物理的模型和机器学习,预测淡水湖中的叶绿素a度

Cheng Chen¹, Qiuwen Chen², Siyang Yao³

¹The National Key Laboratory of Water Disaster Prevention, Nanjing Hydraulic Research Institute, Nanjing 210029, China; College of Water Conservancy and Hydroelectric Power, Hohai University, Nanjing 210098, China; Center for Eco-Environmental Research, Nanjing Hydraulic Research Institute, Nanjing 210029, China.

The Science of the total environment

|October 25, 2023

概括

使用基于物理和机器学习的组合模型预测藻类繁殖,包括贝叶斯模型平均值,显著改善了淡水湖中的素a度预测.

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:

淡水湖中的藻类繁殖是一个全球性的挑战.
准确的甲基 (Chla) 短期预测对于早期预警和减缓至关重要.
结合多个模型来预测Chla仍然是一个未被充分探索的领域.

研究的目的:

开发一种有效的模型,将基于物理和机器学习的方法结合起来,用于Chla预测.
为了提高预测准确性和可靠性,使用贝叶斯模型平均 (BMA) 整体方法.
评估用于短期Chla预测的组合模型的性能.

主要方法:

开发了一种混合模型,将基于物理的模型与机器学习算法 (LSTM,RF,SVM) 集成在一起.
采用贝叶斯模型平均化 (BMA) 进行整体预测.
使用R平方 (R2) 和根平均平方误差 (RMSE) 评估预测准确度,最多为7天的预测.

主要成果:

随着预测时间从1天增加到7天,机器学习预测准确度 (R2) 从0.95降至0.68.
基于物理的建模和LSTM的结合表明了强大的短期Chla预测能力.
对于7天的预测,BMA组合方法实现了强大的性能,产生了最高的R2 (0.834) 和最低的RMSE (0.267μg/L).

结论:

基于物理和LSTM的混合模型有效地预测了Chla.
与单个模型相比,BMA整体预测提高了准确性和可靠性.
BMA方法成功地减少了与单个机器学习模型相关的不确定性.

关键词:

藻类的开花藻类的开花贝叶斯模型的平均值是贝叶斯的模型.叶绿素-一个度度.一个淡水湖的湖泊.机器学习机器学习短期整体预测短期整体预测

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