Estimation missing stream flow data of hydrometric gauge using support vector regression and Ensemble Kalman Filter (EnKF) Technique (Case Study: Upstream Zayanderud Basin)

Articles in Press

Document Type : Original Article

Authors

1 Yasuj university

2 Department of Civil Engineering, Yasouj University

3 Assistant Prof. / Yasouj University

10.22034/iwrr.2024.382843.2735

Abstract

Measuring and recording climate data of gauges are usually used to develop and calibrate hydrological models. Missing hydrological and climate data can cause decreasing models accuracy or disability of developing models. In this study, remaking the missing data of Chelgerd hydrometric gauge located at the upstream of Ghaleshahrokh-Chelgerd sub basin as part of Zayanderud Basin was surveyed. It measures discharge of the first Koohrang tunnel inflow. In order to estimate the missing data, regression support vector machine model was employed and to improve the model performance, Ensemble Kalman Filter (EnKF) was used as data assimilation technique. For evaluating the regression model performance, R, RMSE and Nash-Sutcliff citeria was implemented. The results showed values of 0.83, 3.42, 0.7 for training part and the values of 0.70, 20.38 and 0.25 for test part of the model, for R, RMSE and NS respectively. By using EnKF, the performance of the regression model has been improved and acceptable results were obtained. To modify the EnKF results, the data of Ghaleshahrokh station located at basin outlet as reference station was used alongside a second SVR model. The values of R, RMSE and NS were 0.96, 5.2 and 0.81 for training and 0.76, 6.6 and 0.66 for test stage respectively.

Keywords

Main Subjects

Iran-Water Resources Research

Articles in Press, Accepted Manuscript
Available Online from 01 May 2024

Files

History

  • Receive Date: 05 January 2024
  • Revise Date: 21 April 2024
  • Accept Date: 01 May 2024

Share

How to cite

Statistics