Abstract
Abstract The river stage–discharge relationship has an important impact on modeling, planning, and management of river basins and water resources. In this study, the capability of the Gaussian Process Regressions (GPR) kernel-based approach was assessed in predicting the daily river stage–discharge (RSD) relationship. Three successive hydrometric stations of the Housatonic River were considered, and based on the flow characteristics during the period of 2002–2006 several models were developed and tested via GPR. To enhance the applied model efficiency, two pre-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of the RSD modeling were investigated. In state 1, each station's own data was used and in state 2, the upstream stations’ datasets were used as input to model the RSD downstream of the river. The single and integrated model results showed that the integrated WT- and EEMD-GPR models resulted in more accurate outcomes. Data processing enhanced the models' capability between 25% and 40%. The results showed that the RSD modeling in state 1 led to better results; however, when the station's own data were not available the integrated methods could be applied successfully for the RSD modeling using the previous stations’ data.
Highlights
Accurate prediction of river stage–discharge is of great importance for the utilization and management of sustainable water resources
The models were analyzed with the Gaussian Process Regression (GPR) model to carry out the river discharge prediction
This study assessed the capability of time series pre-processing methods for river daily discharge modeling
Summary
Accurate prediction of river stage–discharge is of great importance for the utilization and management of sustainable water resources. To obtain a continuous record of discharge, the stage is recorded and the discharge is computed from a correlation of stage and measured discharge. This correlation, or training (calibration), is known as the stage–discharge relationship (Baiamonte & Ferro ). Accurate information about the flow rates of rivers is important for a variety of hydrologic applications such as water and sediment bed material load estimation, water resource planning, operation and development, and hydraulic and hydrologic modeling (Wahlin & Clemmens ). Various and complex relationships have been proposed to predict river stage–discharge. Due to the complexity and nonlinearity of the process and validated models, it is difficult to simulate the accurate amount of discharge carried by rivers
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
