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Improving the streamflow prediction accuracy in sparse data regions: a fresh perspective on integrated hydrological-hydrodynamic and hybrid machine learning models
Engineering Applications of Computational Fluid Mechanics ( IF 5.9 ) Pub Date : 2024-08-13 , DOI: 10.1080/19942060.2024.2387051 Saeed Khorram 1 , Nima Jehbez 1
Engineering Applications of Computational Fluid Mechanics ( IF 5.9 ) Pub Date : 2024-08-13 , DOI: 10.1080/19942060.2024.2387051 Saeed Khorram 1 , Nima Jehbez 1
Affiliation
Considering the differences and complex nonlinear relationships of the observational data, this research integrated the hydrological, hydrodynamic and time series models, including the SWAT+, MIKE2...
中文翻译：
提高稀疏数据区域的水流预测精度：集成水文-水动力和混合机器学习模型的新视角
考虑到观测数据的差异性和复杂的非线性关系，本研究集成了水文、水动力和时间序列模型，包括SWAT+、MIKE2……
更新日期：2024-08-13
中文翻译：
提高稀疏数据区域的水流预测精度：集成水文-水动力和混合机器学习模型的新视角
考虑到观测数据的差异性和复杂的非线性关系，本研究集成了水文、水动力和时间序列模型，包括SWAT+、MIKE2……
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