Abstract
Modeling stream flows is vital for water resource planning and flood and drought management. In this study, the performance of hybrid models constructed by combining least square support vector machines (LSSVM), empirical model decomposition (EMD), and particle swarm optimization (PSO) methods in modeling monthly streamflow was evaluated. For establishing the models, 42 years of monthly average streamflow data was used in two hydrometer stations located in the Konya Closed Basin, covering 1964 to 2005. Lagged streamflow values were selected as inputs according to partial autocorrelation values in establishing the models. The dataset was divided into 70% training and 30% testing. Model performances were evaluated according to mean square error, root mean square error, correlation coefficients, scatter plot, and Taylor and Violin diagrams. As a result of the analysis, it was determined that the PSO-LSSVM and EMD-LSSVM models were slightly more successful than the single LSSVM model, and the best model was obtained with the EMD-PSO-LSSVM. In addition, in estimating monthly stream flows, 1-, 9-, 10-, 11-, and 12-month lagged streamflow values were the input combination that gave the best results in semi-arid climatic regions. This result demonstrated that EMD improved the performance of both LSSVM and PSO-LSSVM models by 1% to 5% based on correlation coefficient (R) values.
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Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Adnan, R. M., Mostafa, R. R., Kisi, O., Yaseen, Z. M., Shahid, S., & Zounemat-Kermani, M. (2021). “Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization.” Knowledge-Based Systems, 230, 107379. https://doi.org/10.1016/j.knosys.2021.107379
Chen, W., Chen, X., Peng, J., Panahi, M., & Lee, S. (2021). Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and satin bowerbird optimizer. Geoscience Frontiers, 12(1), 93–107. https://doi.org/10.1016/j.gsf.2020.07.012
Chun-Lin, L. (2010). A tutorial of the wavelet transform. NTUEE, Taiwan, 21, 22.
Dawson, C. W., & Wilby, R. (1998). An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal, 43(1), 47–66. https://doi.org/10.1080/02626669809492102
Dehghani, R., & Poudeh, H. T. (2021). Applying hybrid artificial algorithms to the estimation of river flow: A case study of Karkheh catchment area. Arabian Journal of Geosciences, 14(9), 1–19. https://doi.org/10.1007/s12517-021-07079-2
Demir, V., & Keskin, A. Ü. (2020). Water level change of lakes and sinkholes in Central Turkey under anthropogenic effects. Theoretical and Applied Climatology, 142, 929–943. https://doi.org/10.1007/s00704-020-03347-5
Eberhart, R. C., & Shi, Y. (2000, July). Comparing inertia weights and constriction factors in particle swarm optimization. In Proceedings of the 2000 congress on evolutionary computation. CEC00 (Cat. No. 00TH8512) (Vol. 1, pp. 84–88). IEEE.
Esmaeili-Gisavandani, H., Farajpanah, H., Adib, A., Kisi, O., Riyahi, M. M., Lotfirad, M., & Salehpoor, J. (2021). “Evaluating ability of three types of discrete wavelet transforms for improving performance of different ML models in estimation of daily-suspended sediment load”. Arabian Journal of Geosciences, 15(1). https://doi.org/10.1007/s12517-021-09282-7
Farajpanah, H., Lotfirad, M., Adib, A., Esmaeili-Gisavandani, H., Kisi, Z., Riyahi, M. M., & Salehpoor, J. (2020). Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge. Water Supply, 20(8), 3156–3171. https://doi.org/10.2166/ws.2020.211
Feng, Z. -K., Niu, W. -J., Tang, Z. -Y., Jiang, Z. -Q., Xu, Y., Liu, Y., & Zhang, H. -R. (2020). Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. Journal of Hydrology, 583, 124627. https://doi.org/10.1016/j.jhydrol.2020.124627
Ghosh, S., Das, S., Kundu, D., Suresh, K., Panigrahi, B. K., & Cui, Z. (2012). An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization. Neural Computing and Applications, 21(2), 237–250. https://doi.org/10.1007/s00521-010-0356-x
He, X., Guan, H., & Qin, J. (2015). A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall. Journal of Hydrology, 527, 88–100. https://doi.org/10.1016/j.jhydrol.2015.04.047
Hintze, J. L., & Nelson, R. D. (1998). Violin plots: A box plot-density trace synergism. The American Statistician, 52(2), 181–184.
Huang, N. E., & Wu, Z. (2008). “A review on Hilbert‐Huang transform: Method and its applications to geophysical studies.” Reviews of geophysics, 46(2). https://doi.org/10.1029/2007RG000228
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. -C., Tung, C. C., & Liu, H. H. (1998). “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903–995. https://doi.org/10.1098/rspa.1998.0193
Huang, S., Chang, J., Huang, Q., & Chen, Y. (2014). Monthly streamflow prediction using modified EMD-based support vector machine. Journal of Hydrology, 511, 764–775. https://doi.org/10.1016/j.jhydrol.2014.01.062
Kaleem, M., Guergachi, A., & Krishnan, S. (2021, December 13). “Comparison of empirical mode decomposition, wavelets, and different machine learning approaches for patient-specific seizure detection using signal-derived empirical dictionary approach”. Frontiers in Digital Health, 3. https://doi.org/10.3389/fdgth.2021.738996
Kennedy, J., & Eberhart, R. (1942). “Particle swarm optimization.” Proc., Proceedings of ICNN'95-international conference on neural networks, IEEE, 1948.
Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (Vol. 4, pp. 1942–1948).
Köyceğiz, C., & Büyükyildiz, M. (2019). Temporal trend analysis of extreme precipitation: A case study of Konya Closed Basin. Pamukkale University Journal of Engineering Sciences, 25(8), 956–961.
Liu, P., Li, L., Guo, S., Xiong, L., Zhang, W., Zhang, J., & Xu, C.-Y. (2015). Optimal design of seasonal flood limited water levels and its application for the Three Gorges Reservoir. Journal of Hydrology, 527, 1045–1053. https://doi.org/10.1016/j.jhydrol.2015.05.055
Liu, S., Feng, Z.-K., Niu, W.-J., Zhang, H.-R., & Song, Z.-G. (2019). Peak operation problem solving for hydropower reservoirs by elite-guide sine cosine algorithm with Gaussian local search and random mutation. Energies, 12(11), 2189. https://doi.org/10.3390/en12112189
Mazandaranizadeh, H., & Motahari, M. (2017). Development of a PSO-ANN model for rainfall-runoff response in basins, Case Study: Karaj Basin. Civil Engineering Journal, 3, 35–44.
Meng, E., Huang, S., Huang, Q., Fang, W., Wu, L., & Wang, L. (2019). A robust method for non-stationary streamflow prediction based on improved EMD-SVM model. Journal of Hydrology, 568, 462–478. https://doi.org/10.1016/j.jhydrol.2018.11.015
Meshram, S. G., Ghorbani, M., Deo, R. C., Kashani, M. H., Meshram, C., & Karimi, V. (2019). New approach for sediment yield forecasting with a two-phase feedforward neuron network-particle swarm optimization model integrated with the gravitational search algorithm. Water Resources Management, 33(7), 2335–2356. https://doi.org/10.1007/s11269-019-02265-0
Mohanty, S. R., Kishor, N., & Singh, D. K. (2018, December). “Comparison of empirical mode decomposition and wavelet transform for power quality assessment in FPGA”. In 2018 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) (pp. 1–6). IEEE.
Napolitano, G., Serinaldi, F., & See, L. (2011). Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: An empirical examination. Journal of Hydrology, 406(3–4), 199–214. https://doi.org/10.1016/j.jhydrol.2011.06.015
Okkan, U., & Kırdemir, U. (2016). A water balance model based on budyko framework and its calibration through particle swarm optimization algorithm. Journal of Natural Hazards and Environment, 2(1), 1–10.
Okkan, U., & Kirdemir, U. (2020). Locally tuned hybridized particle swarm optimization for the calibration of the nonlinear Muskingum flood routing model. Journal of Water and Climate Change, 11(S1), 343–358. https://doi.org/10.2166/wcc.2020.015
Orhan, O., Yakar, M., & Ekercin, S. (2020). An application on sinkhole susceptibility mapping by integrating remote sensing and geographic information systems. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-020-05841-6
Özdamar, K. (2004). Statistical data analysis with package programs 1 (p. 649). Kaan Bookstore.
Ravansalar, M., Rajaee, T., & Kisi, O. (2017). Wavelet-linear genetic programming: A new approach for modeling monthly streamflow. Journal of Hydrology, 549, 461–475. https://doi.org/10.1016/j.jhydrol.2017.04.018
Rezaie-Balf, M., Kim, S., Fallah, H., & Alaghmand, S. (2019). Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea. Journal of Hydrology, 572, 470–485. https://doi.org/10.1016/j.jhydrol.2019.03.046
Sadeghi, S., Behnam, H., & Tavakkoli, J. (2014). “Ultrasound elastography using empirical mode decomposition analysis. Journal of Medical Signals and Sensors, 4(1), 18. https://doi.org/10.4103/2228-7477.128434
Samal, N. R., Konar, A., Das, S., & Abraham, A. (2007). “A closed loop stability analysis and parameter selection of the particle swarm optimization dynamics for faster convergence.” Proc., 2007 IEEE Congress on Evolutionary Computation, IEEE, 1769–1776. https://doi.org/10.1109/CEC.2007.4424687
Samanataray, S., & Sahoo, A. (2021). A Comparative study on prediction of monthly streamflow using hybrid ANFIS-PSO approaches. KSCE Journal of Civil Engineering, 25(10), 4032–4043. https://doi.org/10.1007/s12205-021-2223-y
Sang, Y.-F., Wang, Z., & Liu, C. (2012). Period identification in hydrologic time series using empirical mode decomposition and maximum entropy spectral analysis. Journal of Hydrology, 424, 154–164. https://doi.org/10.1016/j.jhydrol.2011.12.044
Sheskin, D. J. (2000). Handbook of parametric and nonparametric statistical procedures Second Edition, CHAPMAN&HALL/CRC, America, 982 s.
Souza, D. P., Martinho, A. D., Rocha, C. C., da S Christo, E., & Goliatt, L. (2022). “Hybrid particle swarm optimization and group method of data handling for short-term prediction of natural daily streamflows.” Modeling Earth Systems and Environment, 1–17. https://doi.org/10.1007/s40808-022-01466-8
Sudheer, C., Maheswaran, R., Panigrahi, B. K., & Mathur, S. (2014). A hybrid SVM-PSO model for forecasting monthly streamflow. Neural Computing and Applications, 24(6), 1381–1389. https://doi.org/10.1007/s00521-013-1341-y
Suykens, J. (2000). Least squares support vector machines for classification and nonlinear modelling. Neural Network World, 10(1), 29–48.
Tan, Q.-F., Lei, X.-H., Wang, X., Wang, H., Wen, X., Ji, Y., & Kang, A.-Q. (2018). An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach. Journal of Hydrology, 567, 767–780. https://doi.org/10.1016/j.jhydrol.2018.01.015
Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal Geophysical Research: Atmospheres, 106(D7), 7183–7192.
Tikhamarine, Y., Souag-Gamane, D., & Kisi, O. (2019). A new intelligent method for monthly streamflow prediction: Hybrid wavelet support vector regression based on grey wolf optimizer (WSVR–GWO). Arabian Journal of Geosciences, 12(17), 1–20. https://doi.org/10.1007/s12517-019-4697-1
Topak, R. (2008). Agriculture-environmental interaction and sustainable water use in Konya Closed Basin. Journal of Konya Commodity Exchange, 30, 6–12.
Vapnik, V. N. (1998). “Adaptive and learning systems for signal processing communications, and control.” Statistical learning theory.
Wang, J., Wang, X., Hui Lei, X., Wang, H., Hua Zhang, X., Jun You, J., Feng Tan, Q., & Lian Liu, X. (2020). “Teleconnection analysis of monthly streamflow using ensemble empirical mode decomposition.” Journal of Hydrology, 582, 124411. https://doi.org/10.1016/j.jhydrol.2019.124411
Wang, K., Wen, X., Hou, D., Tu, D., Zhu, N., Huang, P., Zhang, G., & Zhang, H. (2018). Application of least-squares support vector machines for quantitative evaluation of known contaminant in water distribution system using online water quality parameters. Sensors, 18(4), 938. https://doi.org/10.3390/s18040938
Wang, L., Li, X., Ma, C., & Bai, Y. (2019). Improving the prediction accuracy of monthly streamflow using a data-driven model based on a double-processing strategy. Journal of Hydrology, 573, 733–745. https://doi.org/10.1016/j.jhydrol.2019.03.101
Wu, C., Chau, K. W., & Li, Y. S. (2009). “Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques.” Water Resources Research, 45(8). https://doi.org/10.1029/2007WR006737
Wu, Z., Huang, N. E., Long, S. R., & Peng, C.-K. (2007). On the trend, detrending, and variability of nonlinear and nonstationary time series. Proceedings of the National Academy of Sciences, 104(38), 14889–14894. https://doi.org/10.1073/pnas.0701020104
Yaseen, Z. M., Ebtehaj, I., Bonakdari, H., Deo, R. C., Mehr, A. D., Mohtar, W. H. M. W., Diop, L., El-Shafie, A., & Singh, V. P. (2017). Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. Journal of Hydrology, 554, 263–276. https://doi.org/10.1016/j.jhydrol.2017.09.007
Yu, P.-S., Yang, T.-C., Chen, S.-Y., Kuo, C.-M., & Tseng, H.-W. (2017). Comparison of random forests and support vector machine for real-time radar-derived rainfall forecasting. Journal of Hydrology, 552, 92–104. https://doi.org/10.1016/j.jhydrol.2017.06.020
Zaini, N., Malek, M. A., Yusoff, M., Mardi, N. H., & Norhisham, S. (2018, April). “Daily river flow forecasting with hybrid support vector machine–particle swarm optimization”. In IOP Conference Series: Earth and Environmental Science, 140(1), 012035. IOP Publishing. https://doi.org/10.1088/1755-1315/140/1/012035
Zhang, D., Lin, J., Peng, Q., Wang, D., Yang, T., Sorooshian, S., Liu, X., & Zhuang, J. (2018). Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm. Journal of Hydrology, 565, 720–736. https://doi.org/10.1016/j.jhydrol.2018.08.050
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Thanks to the General Directorate of State Hydraulic Works for providing the streamflow data.
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O. M. Katipoğlu contributed to the data analysis, writing the introduction, results, and conclusions. S. N. Yeşilyurt contributed to the data analysis, writing methods, and results. H. Y. Dalkılıç contributed with data collection and reviewing. F. Akar contributed to data analysis and reviewing. All authors read and approved the final manuscript.
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Katipoğlu, O.M., Yeşilyurt, S.N., Dalkılıç, H.Y. et al. Application of empirical mode decomposition, particle swarm optimization, and support vector machine methods to predict stream flows. Environ Monit Assess 195, 1108 (2023). https://doi.org/10.1007/s10661-023-11700-0
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DOI: https://doi.org/10.1007/s10661-023-11700-0