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Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model

Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonali...

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書誌詳細
主要な著者:	Attar N.F., Pham Q.B., Nowbandegani S.F., Rezaie-Balf M., Fai C.M., Ahmed A.N., Pipelzadeh S., Dung T.D., Nhi P.T.T., Khoi D.N., El-Shafie A.
その他の著者:	57203768412
フォーマット:	論文
出版事項:	MDPI AG 2023
タグ:	タグ追加タグなし, このレコードへの初めてのタグを付けませんか!

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