An improved framework to predict river flow time series data

PeerJ

article

Hafiza Mamona Nazir¹  Ijaz Hussain¹  Ishfaq Ahmad²  Muhammad Faisal³  Ibrahim M. Almanjahie² 

[1] Department of Statistics, Quaid-i-Azam University;Department of Mathematics, College of Science, King Khalid University;Faculty of Health Studies, University of Bradford;Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, University of Bradford

关键词: Empirical Mode Decomposition (EMD);    Wavelet Analysis (WA);    Machine Learning (ML);    Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Empirical Bayes Threshold (EBT);    Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Empirical Bayes Threshold (EBT).;   

DOI  :  10.7717/peerj.7183

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY