Estimating river flows with a new hybrid model

Authors
hamidreza babaali 1
reza dehghani 2
fatemeh dehghani 3
1 Assistant Professor, Department of Civil Engineering, Islamic Azad University, Khorramabad branch, Iran.
2 PhD in Water Sciences and Engineering, Department of Soil Conservation and Watershed Management, Lorestan Province Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension
3 Bachelor student, Department of Civil Engineering, Lorestan University, Iran
10.22034/wmji.2024.2029026.1069
Abstract
River flow prediction is one of the key issues in the management and planning of water resources, in particular the adoption of proper decisions in the event of floods and droughts. To predict the flow rate of rivers, various approaches have been introduced in hydrology, the most important of which are the intelligent models. In this study, a hybrid, model Firefly Algorithm- support vector machine, was applied to estimate the discharge of Dez river basin based on the daily discharge statistics provided by the hydrometric stations located at the upstream of the dam during the statistical period (2002-2022) and its performance was compared with the support vector machine model. The correlation coefficients, root mean square error, and mean absolute error was used for evaluation and a comparison of the performance of models. The results showed that the hybrid structures presented acceptable outcomes in the modeling of river discharge. A comparison of models also showed that the hybrid model of Firefly Algorithm-support vector machine has a better performance in forecasting the flow. In conclusion, the use of the FA-SVM model could be effective in estimating flood peak discharge.
Keywords
Dez Basin
Firefly
Prediction
Support Vector Machine
  1. Adnan, R., Liang, Z., Heddam, S., Kermani, M., Kisi, O., and 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.
  2. Alizadeh, F., Gharamaleki, A., Jalilzadeh, M., and Akhoundzadeh, A. 2020. Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach. Water Resources, 47(3), 41-53.
  3. Basak, D., Pal, S., and Patranabis, D.C. 2007. Support vector regression. Neural Inf. Process, 11(2), 203-225.
  4. Ghorbani, M.A., Khatibi, R., Geol, A., Fazelifard, M.H., and Azani, A. 2016. Modeling river discharge time series using support vector machine and artificial neural networks. Environmental Earth Sciences, 75(4), 675-685
  5. Ghorbani, M.A., Khatibi, R., Karimi, V., Yaseen, Z.M., and 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.
  6. Ghorbani, M.A., Deo, R.C., Karimi, V., Yassen, Z.M., and 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.
  7. Huang, S., Chang, J., Huang, Q., and Chen, Y. 2014. Monthly streamflow prediction using modified emd-based support vector machine. Journal of Hydrology, 511(4), 764-775.
  8. Kisi, O., Karahan, M., and Sen, Z. 2006. River suspended sediment modeling using fuzzy logic approach. Hydrol Process, 20(2), 4351-4362.
  9. Lin, J.Y., Cheng, C.T., and Chau, K.W. 2006. Using support vector machines for long-term discharge prediction. Hydrolog Sci J, 51(3), 599–612.
  10. Liong, S.Y., and Sivapragasam, C. 2002. Flood stage forecasting with support vector machines. J Am Water Resour, 38(4), 173–186.
  11. 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.
  12. 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.
  13. Sedighi, F., Vafakhah, M., and Javadi, MR. 2016. Rainfall–Runoff modeling using support vector machine in snow-affected watershed. Arabian Journal for Science and Engineering, 41(10), 4065-4076.
  14. 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.
  15. 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.
  16. Vapnik, V.N. 1995. The Nature of Statistical Learning Theory. Springer, New York.
  17. Vapnik, V.N. 1998. Statistical learning theory. Wiley, New York
  18. Yang, X.S. 2008. Nature-Inspired Metheuristic Algorithms, Luniver Press.
  19. 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. J Hydrol, 396(4), 128–138.
  20. Zhao, X., Chen, X., Xu, Y., Xi, D., Zhang, Y., and Zheng, X. 2017. An EMD-based chaotic least squares support vector machine hybrid model for annual runoff forecasting. Water, 9(3), 153-164.