Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

Wang, Wenchuan; Chau, Kwok Wing; Xu, Dianxiang; Chen, Xiao Yun

doi:10.1007/s11269-015-0962-6

Cited by 487 publications

(184 citation statements)

References 54 publications

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“…Methods available for correlative modelling range from parametric and non-parametric regression-based approaches to machine-learning techniques Danandeh Mehr et al, 2013;Fan et al, 2015;Okkan and Serbes, 2012;Wang et al, 2015;Wu et al, 2009). For the present study we selected ordinary least squares (OLS) regression because it results in an explicit equation, which facilitates interpretation and comparison with other studies.…”

Section: Model Fittingmentioning

confidence: 99%

Developing and testing a global-scale regression model to quantify mean annual streamflow

Barbarossa

Huijbregts

Hendriks

et al. 2017

Journal of Hydrology

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Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment

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Section: Model Fittingmentioning

confidence: 99%

Developing and testing a global-scale regression model to quantify mean annual streamflow

Barbarossa

Huijbregts

Hendriks

et al. 2017

Journal of Hydrology

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show abstract

“…Furthermore, artificial neural network and other new methods were also used in this research for prediction of precipitation. We will compare and combine these methods in further studies [37][38][39][40][41][42]. The influences of this precipitation fluctuation change to vegetation change will also be further studied.…”

Section: Discussionmentioning

confidence: 99%

Annual Precipitation Fluctuation and Spatial Differentiation Characteristics of the Horqin Region

et al. 2017

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Abstract:Precipitation is the main water source for vegetation survival in arid and semi-arid areas. However, previous studies always focus on the effects of precipitation in different time scales, but ignore the effects of precipitation in different spatial scales. To further study the effects of precipitation fluctuation in different spatial scales, we used the wavelet analysis method to analyze its temporal and spatial change based on data from eighteen meteorological stations during 1961-2015 in Horqin region. Results showed that: (1) from the overall tendency of precipitation changes, the precipitation inter-annual variations in Horqin region had the tendency of gradually decreasing from the southeast (District IV) to the northwest; (2) the precipitation anomalies of District I-IV between 1960 and 1980 were small and approximate to the normal value; (3) in the time scale of 23-32 years, the cyclical fluctuations were very significant and the annual precipitation underwent two cyclical fluctuations from a period of low precipitation to a period of high precipitation; and (4) as results of analyzing the spatial wavelet variance of sub-region, the main cycle of precipitation in District I, District II and District III was between 10 and 11 years, while the main cycle of precipitation in District IV was 25 years. The main conclusions include the following. (1) This region tended to be arid, and the precipitation gradually decreased from the southeast (District IV) to northwest (District I). (2) The influence of spatial differentiation characteristics on precipitation fluctuation in this region was cyclical fluctuation, which gradually decreased from the southeast to the northwest. The length of the cyclical change period gradually shortened. In the first main cycle, whose annual precipitation changes were most significant, the changing characteristic was District IV and District I decreased from 25 years to 10 years. (3) Predicated from the cyclical changing law that the annual precipitation decreases from high to low, the Horqin region will remain in a period of low precipitation between 2016 and 2020.

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“…On the other hand, the BP network is the most widely used training model for the ANN as applied to solve PD classification problems. This is due to its ease of implementation and track record of classifying complex data in other field of applications [61][62][63]. High recognition rates above 90% were recorded with BP, which is a major success of this scheme.…”

Section: Strength Of the Artificial Neural Network Applied To Partiamentioning

confidence: 99%

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

et al. 2016

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Abstract:In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

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Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition

Cited by 487 publications

References 54 publications

Developing and testing a global-scale regression model to quantify mean annual streamflow

Developing and testing a global-scale regression model to quantify mean annual streamflow

Annual Precipitation Fluctuation and Spatial Differentiation Characteristics of the Horqin Region

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

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