干旱区生态环境知识资源中心(Arid): Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Turkiye

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DOI	10.1016/j.jher.2023.07.004
	Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Turkiye
	Katipoglu, Okan Mert; Sarigol, Metin
通讯作者	Katipoglu, OM
来源期刊	JOURNAL OF HYDRO-ENVIRONMENT RESEARCH
ISSN	1570-6443
EISSN	1876-4444
出版年	2023
卷号	50 页码:13-25
英文摘要	Accurate streamflow forecasting is very useful in water resources management, design of hydraulic structures, and almost all issues related to the use of water and water resources, especially in arid regions that have increased in recent years. Since water is the source of all life and the most important basic element for humanity to continue its life, streamflow prediction studies increase its importance daily. This research combined the boosted tree (BT) model with robust empirical mode decomposition, empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, empirical wavelet transforms and variational mode decomposition for predicting daily average streamflow data. While historical streamflow data was input in the model's setup, one-day lead-time streamflow data was used as the target. 70% of the data is reserved for training and the rest for testing. 5-fold cross-validation technique was used to solve the over-fitting problem. The coefficient of determination, mean squared error, Nash-Sutcliffe efficiency and percent bias statistical criteria and Taylor diagrams, polar plot, scattering diagram, and violin plot were used to determine the algorithm's success. At the end of the study, it was found that the most successful streamflow predictions were made with the variational mode decomposition-based BT hybrid approach.
英文关键词	Streamflow prediction Machine learning Variational mode decomposition Empirical mode decomposition Emperical wavelet transform Tigris basin
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001051017400001
WOS关键词	VARIATIONAL MODE DECOMPOSITION ; PERFORMANCE ; RIVER ; PLOT
WOS类目	Engineering, Civil ; Environmental Sciences ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397367
推荐引用方式 GB/T 7714	Katipoglu, Okan Mert,Sarigol, Metin. Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Turkiye[J],2023,50:13-25.
APA	Katipoglu, Okan Mert,&Sarigol, Metin.(2023).Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Turkiye.JOURNAL OF HYDRO-ENVIRONMENT RESEARCH,50,13-25.
MLA	Katipoglu, Okan Mert,et al."Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Turkiye".JOURNAL OF HYDRO-ENVIRONMENT RESEARCH 50(2023):13-25.