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

• Main Authors: Attar, Nasrin Fathollahzadeh, Pham, Quoc Bao, Nowbandegani, Sajad Fani, Rezaie-Balf, Mohammad, Fai, Chow Ming, Ahmed, Ali Najah, Pipelzadeh, Saeed, Dung, Tran Duc, Nhi, Pham Thi Thao, Khoi, Dao Nguyen, El-Shafie, Ahmed
• Format: Article
• Published: MDPI 2020
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.um.edu.my/36985/

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...