Iran-Water Resources Research

Iran-Water Resources Research

Estimation Missing Stream Flow Data of Hydrometric Gauge Using Support Vector Regression and Ensemble Kalman Filter (EnKF) Technique (Case Study: Upstream Zayandehrud Basin)

Document Type : Original Article

Authors
Alireza Kazemzadeh Varzaneh 1
Mehdi Fazeli 2
Hossein Montaseri 3
1 M.Sc. Graduated, Yasouj University, Yasouj, Iran.
2 Assistant Professor, Department of Civil Engineering, Yasouj University, Iran.
3 Associate Professor, Department of Civil Engineering, Yasouj University, Iran.
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 decrease models’ accuracy or impede developing the models. In this study, remaking the missing data of Chelgerd hydrometric gauge located at the upstream of Ghaleshahrokh-Chelgerd sub basin as part of Zayandehrud Basin was surveyed. It measures the discharge from 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 were 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 as reference station located at basin outlet was used alongside a second SVR model. The values of R, RMSE and NS were respectively 0.96, 5.2 and 0.81 for training and 0.76, 6.6 and 0.66 for test stage.
Keywords
Non-Measuring Inflow Discharge
Estimating Missing Data
Support Vector Machine
Regression Model
Data Assimilation

Subjects

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