Closure to “Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence”
This article is a reply.
VIEW THE ORIGINAL ARTICLEPublication: Journal of Hydrologic Engineering
Volume 28, Issue 4
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This paper is supported by the National Natural Science Foundation of China (52009012) and the Fundamental Research Funds for the Central Universities (B210201046). The writers would like to thank the editors and reviewers for their valuable comments and suggestions.
References
Bai, T., L. Wu, J. X. Chang, and Q. Huang. 2015. “Multi-objective optimal operation model of cascade reservoirs and its application on water and sediment regulation.” Water Resour. Manage. 29 (8): 2751–2770. https://doi.org/10.1007/s11269-015-0968-0.
Chang, J., Y. Wang, E. Istanbulluoglu, T. Bai, Q. Huang, D. Yang, and S. Huang. 2015. “Impact of climate change and human activities on runoff in the Weihe River Basin, China.” Quatern. Int. 380–381 (Sep): 169–179. https://doi.org/10.1016/j.quaint.2014.03.048.
Feng, Z. K., W. J. Niu, X. Y. Wan, B. Xu, F. L. Zhu, and J. Chen. 2022a. “Hydrological time series forecasting via signal decomposition and twin support vector machine using cooperation search algorithm for parameter identification.” J. Hydrol. 612 (Part B): 128213. https://doi.org/10.1016/j.jhydrol.2022.128213.
Feng, Z. K., P. F. Shi, T. Yang, W. J. Niu, J. Z. Zhou, and C. T. Cheng. 2022b. “Parallel cooperation search algorithm and artificial intelligence method for streamflow time series forecasting.” J. Hydrol. 606 (Mar): 127434. https://doi.org/10.1016/j.jhydrol.2022.127434.
Huang, S., J. Chang, Q. Huang, and Y. Chen. 2014. “Monthly streamflow prediction using modified EMD-based support vector machine.” J. Hydrol. 511 (Apr): 764–775. https://doi.org/10.1016/j.jhydrol.2014.01.062.
Jun, Z., C. Chun-tian, L. Sheng-li, W. Xin-yu, and S. Jian-jian. 2009. “Daily reservoir inflow forecasting combining QPF into ANNs model.” Hydrol. Earth Syst. Sci. Discuss. 6 (1): 121–150. https://doi.org/10.5194/hessd-6-121-2009.
Karthikeyan, L., and D. Nagesh Kumar. 2013. “Predictability of nonstationary time series using wavelet and EMD based ARMA models.” J. Hydrol. 502 (Oct): 103–119. https://doi.org/10.1016/j.jhydrol.2013.08.030.
Kisi, O., and M. Cimen. 2011. “A wavelet-support vector machine conjunction model for monthly streamflow forecasting.” J. Hydrol. 399 (1–2): 132–140. https://doi.org/10.1016/j.jhydrol.2010.12.041.
Kisi, O., and J. Shiri. 2011. “Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models.” Water Resour. Manage. 25 (13): 3135–3152. https://doi.org/10.1007/s11269-011-9849-3.
Kişi, Ö. 2009. “Neural networks and wavelet conjunction model for intermittent streamflow forecasting.” J. Hydrol. Eng. 14 (8): 773–782. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000053.
Lin, J. Y., C. T. Cheng, and K. W. Chau. 2006. “Using support vector machines for long-term discharge prediction.” Hydrol. Sci. J. 51 (4): 599–612. https://doi.org/10.1623/hysj.51.4.599.
Liu, D., S. Guo, Z. Wang, P. Liu, X. Yu, Q. Zhao, and H. Zou. 2018. “Statistics for sample splitting for the calibration and validation of hydrological models.” Stochastic Environ. Res. Risk Assess. 32 (11): 3099–3116. https://doi.org/10.1007/s00477-018-1539-8.
Napolitano, G., F. Serinaldi, and L. See. 2011. “Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: An empirical examination.” J. Hydrol. 406 (3–4): 199–214. https://doi.org/10.1016/j.jhydrol.2011.06.015.
Shamseldin, A. Y., A. E. Nasr, and K. M. O’Connor. 2002. “Comparison of different forms of the Multi-layer feed-forward neural network method used for river flow forecasting.” Hydrol. Earth Syst. Sci. 6 (4): 671–684. https://doi.org/10.5194/hess-6-671-2002.
Tan, Q. F., X. H. Lei, X. Wang, H. Wang, X. Wen, Y. Ji, and A. Q. Kang. 2018. “An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach.” J. Hydrol. 567 (Dec): 767–780. https://doi.org/10.1016/j.jhydrol.2018.01.015.
Taormina, R., and K. W. Chau. 2015. “ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS.” Eng. Appl. Artif. Intel. 45 (Oct): 429–440. https://doi.org/10.1016/j.engappai.2015.07.019.
Wang, W. C., K. W. Chau, C. T. Cheng, and L. Qiu. 2009. “A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series.” J. Hydrol. 374 (3–4): 294–306. https://doi.org/10.1016/j.jhydrol.2009.06.019.
Wang, W. C., K. W. Chau, L. Qiu, and Y. B. Chen. 2015. “Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.” Environ. Res. 139 (May): 46–54. https://doi.org/10.1016/j.envres.2015.02.002.
Yaseen, Z. M., O. Jaafar, R. C. Deo, O. Kisi, J. Adamowski, J. Quilty, and A. El-Shafie. 2016. “Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq.” J. Hydrol. 542 (Nov): 603–614. https://doi.org/10.1016/j.jhydrol.2016.09.035.
Zhang, X., Y. Peng, C. Zhang, and B. Wang. 2015. “Are hybrid models integrated with data preprocessing techniques suitable for monthly streamflow forecasting? Some experiment evidences.” J. Hydrol. 530 (Nov): 137–152. https://doi.org/10.1016/j.jhydrol.2015.09.047.
Information & Authors
Information
Published In
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Aug 26, 2022
Accepted: Dec 15, 2022
Published online: Feb 9, 2023
Published in print: Apr 1, 2023
Discussion open until: Jul 9, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.