Volume 21, Issue 4 (Winter 2018)                   jwss 2018, 21(4): 143-159 | Back to browse issues page

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Isazadeh M, Mohammadi P, Dinpazhoh Y. Evaluation of Artificial Neural Network and Multiple Linear Regression Models to Estimate the Daily Missing data Flow (Runoff) in (Case Study: Santeh Gauging Station- Kordestan Province). jwss 2018; 21 (4) :143-159
URL: http://jstnar.iut.ac.ir/article-1-3314-en.html
1. Dept. of Water Eng., Faculty of Agric., Tabriz Univ., Tabriz. Iran. , mohammadi.parva@yahoo.com
Abstract:   (7654 Views)
Statistical analysis and forecast discharge data play an important role in management and development of water systems. The most fundamental issues of statistical analysis and forecast discharge in Iran are lack of data in long term period and lack of stream flow data in gauging stations. Considering the issues mentioned in this study, we tried to estimate the daily data flow (runoff) of Santeh gauging station in Kordestan province using the nearby hydrometric and meteorological stations data. This estimation occurred based on the sixteen different input combinations, including data of daily flow of hydrometric stations Safakhaneh and Polanian and daily runoff in Santeh precipitation gauging station. In this research, the daily flow estimation of the Santeh station in each of the months of the year was evaluated for sixteen different combinations and artificial neural network models and multiple linear regressions. The performance of each model was evaluated with the indicators RMSE, CC, NS and t-student statistic. The results showed good performance of both models but the performance of the artificial neural network model was better than the regression model in estimation of the daily runoff in the most months of the year. Mean error of artificial neural network and multiple linear regression models was respectively estimated as 6.31 and 8.07 m3/s in the months of the year. It should be noted that the artificial neural network, for each sixteen combination used, had better result than the regression model.
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Type of Study: Research | Subject: Ggeneral
Received: 2016/08/4 | Accepted: 2017/03/7 | Published: 2018/02/12

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