A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

Xu, Jiang; Chen, Yaning; Bai, Lei; Xu, Yiwen

doi:10.5194/hess-20-1447-2016

Cited by 34 publications

(22 citation statements)

References 53 publications

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“…We set the ensemble number as 100 as suggested by Bai et al [38] and Xu et al [39]. The added white noise has an amplitude, specifically 0.2 times SD of the corresponding data.…”

Section: Eemd Resultsmentioning

confidence: 99%

“…Consequently, the IMF component will become a pure frequency modulation signal that possibly leads to losing its actual meaning. Empirically, a better SD value ranges from 0.2 to 0.3 [39]. When h 1k (t) satisfies the pre-determined SD value, it is called the first IMF component, or IMF1.…”

Section: Emd Methodsmentioning

confidence: 99%

“…Recently, it has extensively been used in a broad range of applications to analyze non-linear and non-stationary signals (or time series), notably in the field of natural sciences, for example, meteorology [38,39].…”

Section: Eemd Methodsmentioning

confidence: 99%

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Revisiting the Granger Causality Relationship between Energy Consumption and Economic Growth in China: A Multi-Timescale Decomposition Approach

Jiang

Bai

2017

Sustainability

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Abstract:The past four decades have witnessed rapid growth in the rate of energy consumption in China. A great deal of energy consumption has led to two major issues. One is energy shortages and the other is environmental pollution caused by fossil fuel combustion. Since energy saving plays a substantial role in addressing both issues, it is of vital importance to study the intrinsic characteristics of energy consumption and its relationship with economic growth. The topic of the nexus between energy consumption and economic growth has been hotly debated for years. However, conflicting conclusions have been drawn. In this paper, we provide a novel insight into the characteristics of the growth rate of energy consumption in China from a multi-timescale perspective by means of adaptive time-frequency data analysis; namely, the ensemble empirical mode decomposition method, which is suitable for the analysis of non-linear time series. Decomposition led to four intrinsic mode function (IMF) components and a trend component with different periods. Then, we repeated the same procedure for the growth rate of China's GDP and obtained four similar IMF components and a trend component. In the second stage, we performed the Granger causality test. The results demonstrated that, in the short run, there was a bidirectional causality relationship between economic growth and energy consumption, and in the long run a unidirectional relationship running from economic growth to energy consumption.

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“…We set the ensemble number as 100 as suggested by Bai et al [38] and Xu et al [39]. The added white noise has an amplitude, specifically 0.2 times SD of the corresponding data.…”

Section: Eemd Resultsmentioning

confidence: 99%

Section: Emd Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Revisiting the Granger Causality Relationship between Energy Consumption and Economic Growth in China: A Multi-Timescale Decomposition Approach

Jiang

Bai

2017

Sustainability

Self Cite

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“…In the decomposition process, the ensemble sample size and the standard deviation of AWGN are respectively set up to 100 and 0.2, based on the previous studies (Tang et al, 2012;Xu et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Ouyang et al (2016) proposed a hybrid modeling approach using EEMD, Support Vector Regression (SVR) and phase-space reconstruction for monthly rainfall forecasting. Xu et al (2016) …”

Section: Introductionmentioning

confidence: 99%

Hydrological Forecasting Using Hybrid Data-Driven Approach

Seo¹,

Kim²

2016

American Journal of Applied Sciences

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This study develops a hybrid model, EEMD-FANN, coupling feed Forward Artificial Neural Network (FANN) and Ensemble Empirical Mode Decomposition (EEMD) for improving the accuracy of daily river stage forecasting. An original river stage data is broken down into a residue and Intrinsic Mode Functions (IMFs) using the EEMD and different FANNs are developed as forecasting models for the decomposed IMFs and residue, respectively. The final forecasted time series is produced by the ensemble aggregation of the forecasted IMFs and residue. The efficiency of EEMD-FANN model is assessed based on the comparison with that of single Adaptive Neuro-Fuzzy Inference System (ANFIS) and FANN to demonstrate the applicability of the hybrid approach in daily river stage forecasting. As a result, it is found that the EEMD-FANN model utilizing time series decomposition by the EEMD and ensemble aggregation produces better performance than the single ANFIS and FANN models using original river stage time series as inputs. The results of this study also signify that the approach coupling the EEMD and FANN can significantly enhance the forecasting ability of the single FANN model and can be utilized as an effective modeling methodology to forecast river stage precisely.

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Unraveling the Hydrology of the Glacierized Kaidu Basin by Integrating Multisource Data in the Tianshan Mountains, Northwestern China

Shen

Fink

Kralisch

et al. 2018

Water Resources Research

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Understanding the water balance, especially as it relates to the distribution of runoff components, is crucial for water resource management and coping with the impacts of climate change. However, hydrological processes are poorly known in mountainous regions due to data scarcity and the complex dynamics of snow and glaciers. This study aims to provide a quantitative comparison of gridded precipitation products in the Tianshan Mountains, located in Central Asia and in order to further understand the mountain hydrology and distribution of runoff components in the glacierized Kaidu Basin. We found that gridded precipitation products are affected by inconsistent biases based on a spatiotemporal comparison with the nearest weather stations and should be evaluated with caution before using them as boundary conditions in hydrological modeling. Although uncertainties remain in this data‐scarce basin, driven by field survey data and bias‐corrected gridded data sets (ERA‐Interim and APHRODITE), the water balance and distribution of runoff components can be plausibly quantified based on the distributed hydrological model (J2000). We further examined parameter sensitivity and uncertainty with respect to both simulated streamflow and different runoff components based on an ensemble of simulations. This study demonstrated the possibility of integrating gridded products in hydrological modeling. The methodology used can be important for model applications and design in other data‐scarce mountainous regions. The model‐based simulation quantified the water balance and how the water resources are partitioned throughout the year in Tianshan Mountain basins, although the uncertainties present in this study result in important limitations.

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A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

Cited by 34 publications

References 53 publications

Revisiting the Granger Causality Relationship between Energy Consumption and Economic Growth in China: A Multi-Timescale Decomposition Approach

Revisiting the Granger Causality Relationship between Energy Consumption and Economic Growth in China: A Multi-Timescale Decomposition Approach

Hydrological Forecasting Using Hybrid Data-Driven Approach

Unraveling the Hydrology of the Glacierized Kaidu Basin by Integrating Multisource Data in the Tianshan Mountains, Northwestern China

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