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A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series
| Content Provider | Scilit |
|---|---|
| Author | Wang, Dong Borthwick, Alistair G. He, Handan Wang, Yuankun Zhu, Jieyu Lu, Yuan Xu, Pengcheng Zeng, Xiankui Wu, Jichun Wang, Lachun Zou, Xinqing Liu, Jiufu Zou, Ying He, Ruimin |
| Copyright Year | 2018 |
| Description | Journal: Environmental Research Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series. |
| Related Links | https://www.pure.ed.ac.uk/ws/files/24514319/Wang_D_et_al._Printout.pdf |
| Ending Page | 281 |
| Page Count | 13 |
| Starting Page | 269 |
| ISSN | 00139351 |
| e-ISSN | 10960953 |
| DOI | 10.1016/j.envres.2017.09.033 |
| Journal | Environmental Research |
| Volume Number | 160 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2018-01-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Environmental Research Data-driven Model Hydro-meteorological Series Rank-set Pair Analysis Wavelet De-noising |
| Content Type | Text |
| Resource Type | Article |
| Subject | Biochemistry Environmental Science |
