Abstract
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.
Highlights
The evaluation and prediction of surface water quality is one of the central challenges in the water resource industries today
This study developed ANN and Least Square Support Vector Machine (LS-SVM) techniques, to predict the Total Dissolved Solid (TDS) of rivers, i.e., Aji Chay River
To assess the performance of used methods, Root mean square error (RMSE), correlation coefficient (R2) and NSE were applied as performance indicators for the analysis
Summary
The evaluation and prediction of surface water quality is one of the central challenges in the water resource industries today. The ANN is one such black box model with a high potential for prediction in complicated non-linear systems This technique requires a training or calibration phase, and generally estimates the amount of qualitative and quantitative parameters. It is relatively accurate in determining the standard deviation of data, and has the capability of modelling the fundamental relationship between the inputs and outputs with a generalization potential [17,18]. When excessive numbers of variables are exploited as inputs, the most correlated variables logically dominate the model and, it is not possible to utilize all the physical knowledge or available measurements. This can be solved by pre-processing techniques which select the most sensitive variables and, reduce the input space [19, 20]
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