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Complexity
Volume 2019, Article ID 2782715, 14 pages
https://doi.org/10.1155/2019/2782715
Research Article

Development of Multidecomposition Hybrid Model for Hydrological Time Series Analysis

1Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
2Faculty of Health Studies, University of Bradford, Bradford BD7 1DP, UK
3Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
4Arriyadh Community College, King Saud University, Riyadh, Saudi Arabia
5KSA Workers University, El-Mansoura, Egypt
6College of Business Administration, King Saud University, Al-Muzahimiyah, Saudi Arabia
7Department of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi Arabia
8Department of Mathematics and Statistics, Faculty of Basic and Applied Sciences, International Islamic University, 44000 Islamabad, Pakistan

Correspondence should be addressed to Ijaz Hussain; kp.ude.uaq@zaji

Received 1 October 2018; Accepted 13 December 2018; Published 2 January 2019

Guest Editor: Pedro Palos

Copyright © 2019 Hafiza Mamona Nazir et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. P. Solomatine and A. Ostfeld, “Data-driven modelling: some past experiences and new approaches,” Journal of Hydroinformatics, vol. 10, no. 1, pp. 3–22, 2008. View at Publisher · View at Google Scholar
  2. C. Di, X. Yang, and X. Wang, “A four-stage hybrid model for hydrological time series forecasting,” PLoS ONE, vol. 9, no. 8, Article ID e104663, 2014. View at Publisher · View at Google Scholar
  3. Z. Islam, Literature Review on Physically Based Hydrological Modeling [Ph. D. thesis], pp. 1–45, 2011.
  4. A. R. Ghumman, Y. M. Ghazaw, A. R. Sohail, and K. Watanabe, “Runoff forecasting by artificial neural network and conventional model,” Alexandria Engineering Journal, vol. 50, no. 4, pp. 345–350, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Riad, J. Mania, L. Bouchaou, and Y. Najjar, “Rainfall-runoff model usingan artificial neural network approach,” Mathematical and Computer Modelling, vol. 40, no. 7-8, pp. 839–846, 2004. View at Publisher · View at Google Scholar
  6. T. Peng, J. Zhou, C. Zhang, and W. Fu, “Streamflow Forecasting Using Empirical Wavelet Transform and Artificial Neural Networks,” Water, vol. 9, no. 6, p. 406, 2017. View at Publisher · View at Google Scholar
  7. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, Calif, USA, 1970. View at MathSciNet
  8. B. N. S. Ghimire, “Application of ARIMA Model for River Discharges Analysis,” Journal of Nepal Physical Society, vol. 4, no. 1, pp. 27–32, 2017. View at Google Scholar
  9. Ö. Kişi, “Streamflow forecasting using different artificial neural network algorithms,” Journal of Hydrologic Engineering, vol. 12, no. 5, pp. 532–539, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. C. A. G. Santos and G. B. L. D. Silva, “Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models,” Hydrological Sciences Journal, vol. 59, no. 2, pp. 312–324, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. C. Wu, K. Chau, and Y. Li, “Methods to improve neural network performance in daily flows prediction,” Journal of Hydrology, vol. 372, no. 1-4, pp. 80–93, 2009. View at Publisher · View at Google Scholar
  12. T. Partal, “Wavelet regression and wavelet neural network models for forecasting monthly streamflow,” Journal of Water and Climate Change, vol. 8, no. 1, pp. 48–61, 2017. View at Publisher · View at Google Scholar
  13. Z. M. Yaseen, M. Fu, C. Wang, W. H. Mohtar, R. C. Deo, and A. El-shafie, “Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons,” Water Resources Management, vol. 32, no. 5, pp. 1883–1899, 2018. View at Publisher · View at Google Scholar
  14. M. Rezaie-Balf and O. Kisi, “New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine,” Hydrology Research, vol. 49, no. 3, pp. 939–953, 2018. View at Publisher · View at Google Scholar
  15. H. Liu, C. Chen, H.-Q. Tian, and Y.-F. Li, “A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks,” Journal of Renewable Energy, vol. 48, pp. 545–556, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Qu, K. Zhang, J. Wang, W. Zhang, and W. Leng, “A Hybrid model based on ensemble empirical mode decomposition and fruit fly optimization algorithm for wind speed forecasting,” Advances in Meteorology, 2016. View at Google Scholar · View at Scopus
  17. Y. Sang, “A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series,” Water Resources Management, vol. 26, no. 11, pp. 3345–3365, 2012. View at Publisher · View at Google Scholar
  18. N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 454, Article ID 1971, pp. 903–995, 1998. View at Google Scholar
  19. Z. H. Wu and N. E. Huang, “A study of the characteristics of white noise using the empirical mode decomposition method,” Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 460, no. 2046, pp. 1597–1611, 2004. View at Publisher · View at Google Scholar
  20. Z. Wang, J. Qiu, and F. Li, “Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting,” Water, vol. 10, no. 7, p. 853, 2018. View at Publisher · View at Google Scholar
  21. N. A. Agana and A. Homaifar, “EMD-based predictive deep belief network for time series prediction: an application to drought forecasting,” Hydrology, vol. 5, no. 1, p. 18, 2018. View at Google Scholar
  22. A. Kang, Q. Tan, X. Yuan, X. Lei, and Y. Yuan, “Short-term wind speed prediction using EEMD-LSSVM model,” Advances in Meteorology, 2017. View at Google Scholar
  23. Z. H. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis (AADA), vol. 1, no. 1, pp. 1–41, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. W.-C. Wang, K.-W. Chau, D.-M. Xu, and X.-Y. Chen, “Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition,” Water Resources Management, vol. 29, no. 8, pp. 2655–2675, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Su, H. Li, Z. Chen, and Z. Wen, “An approach using ensemble empirical mode decomposition to remove noise from prototypical observations on dam safety,” SpringerPlus, vol. 5, no. 1, 2016. View at Publisher · View at Google Scholar
  26. X.-S. Jiang, L. Zhang, and M. X. Chen, “Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China,” Transportation Research Part C: Emerging Technologies, vol. 44, pp. 110–127, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Dai, D. Niu, and Y. Li, “Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm,” Energies, vol. 11, no. 1, p. 163, 2018. View at Publisher · View at Google Scholar
  28. M. E. Torres, M. A. Colominas, G. Schlotthauer, and P. Flandrin, “A complete ensemble empirical mode decomposition with adaptive noise,” in Proceedings of the 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 4144–4147, Prague, Czech Republic, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. A. Jayawardena and A. Gurung, “Noise reduction and prediction of hydrometeorological time series: dynamical systems approach vs. stochastic approach,” Journal of Hydrology, vol. 228, no. 3-4, pp. 242–264, 2000. View at Publisher · View at Google Scholar
  30. M. Yang, Y. Sang, C. Liu, and Z. Wang, “Discussion on the Choice of Decomposition Level for Wavelet Based Hydrological Time Series Modeling,” Water, vol. 8, no. 5, p. 197, 2016. View at Publisher · View at Google Scholar
  31. J. Kim, C. Chun, and B. H. Cho, “Comparative analysis of the DWT-based denoising technique selection in noise-riding DCV of the Li-Ion battery pack,” in Proceedings of the 2015 9th International Conference on Power Electronics and ECCE Asia (ICPE 2015-ECCE Asia), pp. 2893–2897, Seoul, South Korea, June 2015. View at Publisher · View at Google Scholar
  32. H. Ahmadi, M. Mottaghitalab, and N. Nariman-Zadeh, “Group Method of Data Handling-Type Neural Network Prediction of Broiler Performance Based on Dietary Metabolizable Energy, Methionine, and Lysine,” The Journal of Applied Poultry Research, vol. 16, no. 4, pp. 494–501, 2007. View at Publisher · View at Google Scholar
  33. A. S. Shakir and S. Ehsan, “Climate Change Impact on River Flows in Chitral Watershed,” Pakistan Journal of Engineering and Applied Sciences, 2016. View at Google Scholar
  34. B. Khan, M. J. Iqbal, and M. A. Yosufzai, “Flood risk assessment of River Indus of Pakistan,” Arabian Journal of Geosciences, vol. 4, no. 1-2, pp. 115–122, 2011. View at Publisher · View at Google Scholar
  35. K. Gaurav, R. Sinha, and P. K. Panda, “The Indus flood of 2010 in Pakistan: a perspective analysis using remote sensing data,” Natural Hazards, vol. 59, no. 3, pp. 1815–1826, 2011. View at Publisher · View at Google Scholar
  36. A. Sarwar and A. S. Qureshi, “Water management in the indus basin in Pakistan: challenges and opportunities,” Mountain Research and Development, vol. 31, no. 3, pp. 252–260, 2011. View at Google Scholar
  37. G. Pappas, “Pakistan and water: new pressures on global security and human health,” American Journal of Public Health, vol. 101, no. 5, pp. 786–788, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. J. L. Wescoat, A. Siddiqi, and A. Muhammad, “Socio-Hydrology of Channel Flows in Complex River Basins: Rivers, Canals, and Distributaries in Punjab, Pakistan,” Water Resources Research, vol. 54, no. 1, pp. 464–479, 2018. View at Publisher · View at Google Scholar