Estimation of Monthly Total Dissolved Solids Using ANN and LS-SVM Techniques in the Aji Chay River, Iran
| Autor: | Aida Bagheri Basmanji, Mahdieh JannatKhah, Ebrahim Rahmani, Jonathan Peter Cox, Abolghasem Akbari |
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| Rok vydání: | 2021 |
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| Zdroj: | Journal of Civil Engineering and Construction. 10:1-10 |
| ISSN: | 2051-7777 2051-7769 |
| DOI: | 10.32732/jcec.2021.10.1.1 |
| Popis: | This research follows on from diverse international efforts to safeguard one of the largest natural lakes in the world, Urmia lake in North West Iran. In this research two new numerical packages based on Artificial Neural Networks (ANN) and the Least Square Support Vector Machine (LS-SVM) models were developed to estimate monthly Total Dissolved Solid (TDS) in the Aji Chay River, one the main tributaries of Urmia lake, Iran. A feed forward back propagation (FFB) model was used to obtain a set of coefficients for a linear model, and the radial basis function (RBF) kernel was employed for the LS-SVM model. The input data sets of both the ANN and LS-SVM models consists of six water quality parameters: TDS, Mg2+, Na+, Ca2+, Cl-, and SO4 2-, all collected on a monthly time scale over a period of 30 years from the Vanyar and Zarnagh stations, in the Aji Chay watershed. The research demonstrated that both models can effectively predict the variability of TDS, but for the Vanyar station with the ANN model (giving an R2 value of 0.913 and RMSE of 0.0032, a Nash-Sutcliffe Efficiency (NSE) coefficient 0.812 and as such has a more efficient and accurate estimation when compared to the LS-SVM model with R2=0.871 and RMSE =0.097 and NSE=0.86. The analysis of Zarnagh station data shows R2=0.853 and RMSE=0.0162, NSE= 0.854 for SVM and R2=0.903 and RMSE =0.0091 and NSE=0.85 for ANN. |
| Databáze: | OpenAIRE |
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