Application of Hybrid Model of Artificial Neural Networks and Firefly Algorithm to Predict the Amount of TDS in River Water

Subject Areas : Water resources management

Farahnaz Sabzevari ¹ , Behrouz Yaghoubi ² , Saeid Shabanlou ³

1 - Master Science Student, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
2 - Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
3 - Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.

Received: 2023-02-13 Accepted : 2023-07-09 Published : 2023-06-22

Keywords: TDS, Artificial Neural Networks, Firefly algorithm, Gavehrood River,

Abstract :

Background and Aim: Estimation and forecasting of qualitative parameters along with quantitative parameters of water alongside the river to make correct managerial decisions is one of the objectives of managers and planners of the water industry should be accurately simulated. Most of the models for qualitative parameter estimations require very large input parameters that are either difficult to access or require much time and money to determine. Therefore, the use of data-driven models in this field has been developed to save time and money.Method: In this paper, the application of artificial neural networks and its combination with the firefly algorithm to predict the amount of Total dissolved solids (TDS) of water in the Gavehrood River located in Iran, Kermanshah has been trained and validated. with this purpose, water quality data of hydrometric station upstream of the Gavoshan reservoir dam are used for the statistical period (1991-2010). Based on different inputs, the multilayer perceptron (MLP) artificial neural network and its combination with the firefly algorithm are tested. The best algorithm of the inputs, the number of hidden layers and the number of neurons in each layer in the artificial neural network are determined. The input data imported to the models include the flow rate (Q), Sodium (Na), Magnesium (Mg), Calcium (Ca), Sulfate (So4), Chloride (Cl), Bicarbonate (Ho3), Electrical conductivity (EC) and Total Dissolved Solides of the river in the previous period (TDSt-1) and the output data of TDS. The number of hidden layers is obtained to be 1 and the number of hidden layer neurons is achieved to be 9. Also, the neural network function in this study is considered as a waterfall type and the results are compared by combining artificial neural networks with the firefly algorithm. The model outputs are compared with measurement data using the error measurement criteria.Results: In this regard, the values of the used error evaluation indices including the observed standard deviation (RSR), Nash Sutcliffe coefficient (NSC), correlation coefficient (R) and root mean square error (MSE) for artificial neural network are yielded 0.154, 0.976, 0.989 and 25.27, respectively and in the case of the neural network combination with the firefly algorithm, are achieved to be 0.129, 0.983, 0.992 and 17.8, respectively.Conclusion: Therefore, the performance of the hybrid method of artificial neural networks by using the firefly algorithm in predicting TDS is more appropriate than artificial neural networks.

References:

Ebtehaj, I., Bonakdari, H., Shamshirband, S., Mohammadi, K. (2016). A combined support vector machine-wavelet transform model for prediction of sediment transport in sewer. Flow Measurement and Instrumentation, 47, 19-27.

Fletcher, D., Goss, E. (1993). Forecasting with neural networks: an application using bankruptcy data. Information & Management, 24(3), 159-167.

Hornik, K., Stinchcombe, M., White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366.

Huiqun, M., Ling, L. (2008). Water quality assessment using artificial neural network. Paper presented at the Computer Science and Software Engineering, 2008 International Conference on.

Kurunç, A., K. Yürekli and O. Cevik (2005). "Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey." Environmental Modelling & Software 20(9): 1195-1200.

Li, R. Z. (2006). Advance and trend analysis of theoretical methodology for water quality forecast. Journal of Hefei University of Technology, 29(1), 26-30.

Mohaghegh, A., M. Valikhan Anaraki and S. Farzin (2020). "Modeling of qualitative parameters (Electrical conductivity and total dissolved solids) of Karun river at Mollasani, Ahvaz and Farsiat stations using data mining methods." Iranian Journal of Health and Environment 13(1): 103-122. [in Persian]

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885-900.

Najah, A., Elshafie, A., Karim, O. A., Jaffar, O. (2009). Prediction of Johor River water quality parameters using artificial neural networks. European Journal of Scientific Research, 28(3), 422-435.

Pipelzadeh, S., R. Mastouri and N. Shahkarami (2022). "Modeling total suspended solids using artificial intelligence methods based on pre processing algorithms." Iranian Water Researches Journal 16(1): 25-37. [in Persian]

Raheli, B., Aalami, M. T., El-Shafie, A., Ghorbani, M. A., Deo, R. C. (2017). Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environmental Earth Sciences, 76(14).

Rezaei, E., B. Shahinejad and H. Yonesi (2019). "Analysis and evaluation of effective parameters on the amount of Total Dissolved Solids in Rivers." Watershed Engineering and Management 11(1): 147-165. [in Persian]

Sarkar, A., Pandey, P. (2015). River water quality modelling using artificial neural network technique. Aquatic Procedia, 4, 1070-1077.

Singh, K. P., Basant, A., Malik, A., Jain, G. (2009). Artificial neural network modeling of the river water quality—a case study. Ecological modelling, 220(6), 888-895.

Wang, Z., Fu, Q., Jiang, Q. (2009). Projection pursuit model based on particle swarm optimization algorithm and its application on water quality evaluation. Paper presented at the Comprehensive Evaluation of Economy and Society with Statistical Science, Conference Proceedings of 2009 International Institute of Applied Statistics Studies.

Yang, X.-S. (2009). Firefly algorithms for multimodal optimization. Paper presented at the International symposium on stochastic algorithms.

Zamanzad Ghavidel, S., M. Montaseri and H. Sanikhani (2017). "Moldeling Of Dissolved Solids By Using Hybrid Soft Computing Methods (Case Study: Nazluchay Basin)." Iranian journal of Ecohydrology 4(4): 983-996. [in Persian]

Zhang, Q., Li, Z., Zeng, G., Li, J., Fang, Y., Yuan, Q. (2009a). Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China. 152(1-4), 123.

Zhang, Y., Guo, F., Meng, W., Wang, X.-Q. J. E. m. (2009b). Water quality assessment and source identification of Daliao river basin using multivariate statistical methods. 152(1-4), 105.