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Paper
Title

Prediction of Water Quality of Ajichay River using developed Artificial Neural Network and Supporting Vector Machine Models

Year: 1395
Publish place:

COI: ICESCON03_084
Language: EnglishView: 571
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Authors

Mahdiye Janatkhah - Teacher at Islamic Azad University of Tabriz
Aida Bagheri Basmenji - M.sc. student of Tabriz University, Civil Engineering, Water Resources Engineering
Ebrahim Rahmani - Expert in water and wastewater

Abstract:

International efforts have been launched to save the endangered Urmia Lake as one of the largest natural lake worldwide. Ajichay River located in East Azarbaijan province, Iran, is one of the main rivers discharging into this lake and, thus, its water quality can directly affect the Urmia Lake ecosystem and life. In this research, we develop and propose two new numerical packages on the basis of Artificial Neural Network (ANN) and Supporting Vector Machine (SVM) models to estimate the monthly Total Dissolved Solid (TDS) of Ajichay’s water. For the ANN calibration, the feed forward back prop (FFB) model is used to obtain a set of coefficients for a linear model, and the radial basic function (RBF) kernel was used for the SVM model. The input data sets of both ANN and SVM models consist of six water quality parameters: TDS, Mg, Na, Ca, Cl, and SO4 collected monthly over a period of 30 years at Vanyar station situated on the banks of Ajichay River. Both models can successfully predict the variability of water’s TDS, but the ANN model with R2=0.958 and RMSE=0.0043 has a more efficient and accurate estimation compared to the SVM model with R2= 0.84 and RMSE = 0.009.

Keywords:

Paper COI Code

This Paper COI Code is ICESCON03_084. Also You can use the following address to link to this article. This link is permanent and is used as an article registration confirmation in the Civilica reference:

https://civilica.com/doc/491530/

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Janatkhah, Mahdiye and Bagheri Basmenji, Aida and Rahmani, Ebrahim,1395,Prediction of Water Quality of Ajichay River using developed Artificial Neural Network and Supporting Vector Machine Models,سومین کنفرانس بین المللی علوم و مهندسی,https://civilica.com/doc/491530

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Scientometrics

The specifications of the publisher center of this Paper are as follows:
Type of center: Azad University
Paper count: 13,084
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