Evaluation of Meta-heuristics Hybrid Models for the River Flow Simulation (Case Study: The River Kashkan, Lorestan, Iran

Younesi, Hojatolah; Godarzi, Ahmad; Shakarami, Masoud

doi:10.22034/hyd.2021.48053.1605

Document Type : پژوهشی

Authors

¹ Assistant Professor of Water Engineering Department

² PhD student Water structures

³ Assistant Professor, Department of Water Engineering, Faculty of Agriculture, Lorestan University

https://doi.org/10.22034/hyd.2021.48053.1605

Abstract

Today, hybrid models of artificial intelligence are considered as a suitable method for simulating hydrological phenomena, including quantitative estimation of river flow. For this purpose, there are various approaches in hydrology to estimate the flow rate of rivers, of which artificial intelligence models are the most important. Therefore, in this study, the performance of support vector-wavelet regression, backup vector-gray wolf regression and bat-support vector regression models to simulate the flow of Kashkan river located in Lorestan province during the statistical period of 2010-2011 in the daily time scale were analyzed. The criteria of correlation coefficient, root mean square error and mean absolute value of error and bias were selected for evaluation and performance of the models. The results showed that the hybrid models have acceptable results in simulating the river discharge. Comparison of models also showed that the support-wavelet vector regression model in the validation stage showed values of R2 = 0.960, RMSE = 0.045, MAE = 0.024, NS = 0.968 and BIAS = 0.001 in predicting daily river flow. . Overall, the results showed that the use of hybrid support-wavelet regression model can be useful in predicting daily discharge.

Keywords

20.1001.1.23833254.1400.8.29.4.9

Main Subjects

Geomorphology

References

Adnan, R., Liang, Z., Heddam, S., Kermani, M., Kisi, O., Li, B. (2019). Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology, 19(4): 432-448.

Alizadeh, F., Gharamaleki, A., Jalilzadeh, M., Akhoundzadeh, A. (2020). Prediction of river stage-discharge process based on a conceptual model using EEMD-WT-LSSVM. Approach Water Resources, 47: 41-53.

Amuda, A., Brest, J., Mezura-Montes, E. (2013). Structured Population Size Reduction Differential Evolution with Multiple Mutation Strategies on CEC 2013 real parameter optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, Cancun, Mexico, 1925–1931.

Babaali, H., Dehghani, R. (2017). The Prediction of the Flood Peak Discharge Using a Wavelet Neural Network. Journal of Hydrogeomorphology, 4(11): 149-168. (In Persian).

Basak, D., Pal, S., and Patranabis, D.C. (2007). Support vector regression. Neural Inf Process, 11(2): 203-225.

Dehghani, R., Torabi, H. (2021). Applying hybrid artificial algorithms to the estimation of river flow: a case study of Karkheh catchment area. Arabian Journal of Geosciences, 14: 768-780

Dehghani, R., Torabi, H., Younesi, H., Shahinejad, B. (2020). Application of Hybrid Support Vector machine models in Predicting River Flow Karkhe basin. Journal of Hydrogeomorphology, 7(22): 155-175. (In Persian).

Dehghani, R., Younesi, H., Torabi Poudeh, H. (2016). Comparing the performance of Support Vector Machines, Gene Expression Programming and Bayesian networks in predicting river flow (Case study: Kashkan River), Journal of Soil and Water Conservation Research, 24(4):161-177. (in Persian).

Ghorbani, M.A., Deo, R., Karimi, V., Yassen, Z.M., Terzi, O. (2018). Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey, Stochastic, Environmental Research and Risk Assessment, 32(6), 1683-1697.

Ghorbani, M.A., Khatibi, R., Karimi, V., Yaseen, Z.M., Zounemat-Kermani,M. (2018). Learning from multiple models using artificial intelligence to improve model prediction accuracies: Application to River Flows, Water Resour Management, 32(13): 4201-4215.

Hamel, L. (2009). Knowledge discovery with support vector Machines, Hoboken, N.J. John Wiley.

Kisi, O., Karahan, M., and Sen, Z. (2006). River suspended sediment modeling using fuzzy logic approach, Hydrology of Process, 20(2): 4351-4362.

Misra, D., Oommen, T., Agarwa, A., Mishra, S.K., and Thompson, A.M. (2009). Application and analysis of support vector machine based simulation for runoff and sediment yield, Biosyst Eng, 103(3): 527–535.

Nagy, H., Watanabe, K., and Hirano, M. (2002). Prediction of sediment load concentration in rivers using artificial neural network model, Journal of Hydraulics Engineering, 128(3): 558-559.

Ostu, N. (1979). A Threshold Selection Method from Gray-Level Histograms [J]. IEEE Transactions on Systems Man and Cybernetics, 9 (1): 62-66.

Salehi, M., Radmanesh, F., Zarei, H., Mansouri, B., Solgi, A. (2016). A combined Time Series – Wavelet Model for Prediction of Ground Water Level (Case Study: Firuzabad Plain), Irrigation Science and Engineering, 41(4): 1-16. (in Persian)

Shin, S., Kyung, D., Lee, S., Taik & Kim, J., and Hyun, J. (2005). An application of support vector machines in bankruptcy prediction model, Expert Systems with Applications, 28(4); 127-135.

Vapnik, V., and Chervonenkis, A. (1991). The necessary and sufficient conditions for consistency in the empirical risk minimization method, Pattern Recognition and Image Analysis, 1(3): 283-305.

Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. Springer, New York

Vapnik, V.N. (1998). Statistical learning theory. Wiley, New York.

Wang, D., Safavi, A.A., and Romagnoli, J.A. (2000). Wavelet-based adaptive robust M-estimator for non-linear system identification, AIChE Journal, 46(4): 1607-1615.

Yoon, H., Jun, S.C., Hyun, Y., Bae, G.O., and Lee, K.K. (2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer, Journal of Hydrol, 396(4): 128–138.

Evaluation of Meta-heuristics Hybrid Models for the River Flow Simulation (Case Study: The River Kashkan, Lorestan, Iran

References

References

Volume 8, Issue 29 - Serial Number 29March 2022Pages 86-69

Volume 8, Issue 29 - Serial Number 29
March 2022
Pages 86-69