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Volume 2019, Article ID 2782715, 14 pages
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.


Accurate prediction of hydrological processes is key for optimal allocation of water resources. In this study, two novel hybrid models are developed to improve the prediction precision of hydrological time series data based on the principal of three stages as denoising, decomposition, and decomposed component prediction and summation. The proposed architecture is applied on daily rivers inflow time series data of Indus Basin System. The performances of the proposed models are compared with traditional single-stage model (without denoised and decomposed), the hybrid two-stage model (with denoised), and existing three-stage hybrid model (with denoised and decomposition). Three evaluation measures are used to assess the prediction accuracy of all models such as Mean Relative Error (MRE), Mean Absolute Error (MAE), and Mean Square Error (MSE). The proposed, three-stage hybrid models have shown improvement in prediction accuracy with minimum MRE, MAE, and MSE for all case studies as compared to other existing one-stage and two-stage models. In summary, the accuracy of prediction is improved by reducing the complexity of hydrological time series data by incorporating the denoising and decomposition.