An improved framework to predict river flow time series data.

Autor:	Nazir HM; Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan., Hussain I; Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan., Ahmad I; Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia., Faisal M; Faculty of Health Studies, University of Bradford, Bradford, United Kingdom.; Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Bradford, Bradford, United Kingdom., Almanjahie IM; Department of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia.
Jazyk:	angličtina
Zdroj:	PeerJ [PeerJ] 2019 Jul 01; Vol. 7, pp. e7183. Date of Electronic Publication: 2019 Jul 01 (Print Publication: 2019).
DOI:	10.7717/peerj.7183
Abstrakt:	Due to non-stationary and noise characteristics of river flow time series data, some pre-processing methods are adopted to address the multi-scale and noise complexity. In this paper, we proposed an improved framework comprising Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Empirical Bayesian Threshold (CEEMDAN-EBT). The CEEMDAN-EBT is employed to decompose non-stationary river flow time series data into Intrinsic Mode Functions (IMFs). The derived IMFs are divided into two parts; noise-dominant IMFs and noise-free IMFs. Firstly, the noise-dominant IMFs are denoised using empirical Bayesian threshold to integrate the noises and sparsities of IMFs. Secondly, the denoised IMF's and noise free IMF's are further used as inputs in data-driven and simple stochastic models respectively to predict the river flow time series data. Finally, the predicted IMF's are aggregated to get the final prediction. The proposed framework is illustrated by using four rivers of the Indus Basin System. The prediction performance is compared with Mean Square Error, Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Our proposed method, CEEMDAN-EBT-MM, produced the smallest MAPE for all four case studies as compared with other methods. This suggests that our proposed hybrid model can be used as an efficient tool for providing the reliable prediction of non-stationary and noisy time series data to policymakers such as for planning power generation and water resource management. Competing Interests: The authors declare there are no competing interests.
Databáze:	MEDLINE
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