Singular spectrum analysis and forecasting of hydrological time series

Marques, Carlos; Ferreira, J. A.; Rocha, Alfredo; Castanheira, J. M.; Melo-Gonçalves, P.; Vaz, Nuno; Dias, João Miguel

doi:10.1016/j.pce.2006.02.061

Cited by 96 publications

(57 citation statements)

References 31 publications

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“…Monthly streamflow time series not only exhibits stochastic characteristics but also seasonal and periodic patterns. Entropy spectral analysis can extract important information of time series, such as the periodic characteristics [6][7][8][9][10][11]. Therefore, combining entropy spectral theory with time series analysis provides a new way for streamflow forecasting.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Entropy Spectral Analysis Methods for Streamflow Forecasting in Northwest China

Zhou

Juan-li

et al. 2017

Entropy

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Abstract:Monthly streamflow has elements of stochasticity, seasonality, and periodicity. Spectral analysis and time series analysis can, respectively, be employed to characterize the periodical pattern and the stochastic pattern. Both Burg entropy spectral analysis (BESA) and configurational entropy spectral analysis (CESA) combine spectral analysis and time series analysis. This study compared the predictive performances of BESA and CESA for monthly streamflow forecasting in six basins in Northwest China. Four criteria were selected to evaluate the performances of these two entropy spectral analyses: relative error (RE), root mean square error (RMSE), coefficient of determination (R 2 ), and Nash-Sutcliffe efficiency coefficient (NSE). It was found that in Northwest China, both BESA and CESA forecasted monthly streamflow well with strong correlation. The forecast accuracy of BESA is higher than CESA. For the streamflow with weak correlation, the conclusion is the opposite.

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Section: Introductionmentioning

confidence: 99%

Comparison of Two Entropy Spectral Analysis Methods for Streamflow Forecasting in Northwest China

Zhou

Juan-li

et al. 2017

Entropy

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“…rainfall, discharge, temperature) can be found in Hanson et al [17] and Marques et al [18]. This method can be used particularly to extract the main components of rainfall and discharge series and to provide good forecast for them [18]. Sivapragasam et al [14] combined SSA with the support vector machine method (the latter called SSA-SVM approach) to predict rainfall at Station 23 (Singapore) and runoff from Tryggevaelde catchment (Denmark), and the results were compared with those of the non-linear prediction (NLP) method.…”

Section: Introductionmentioning

confidence: 89%

“…The application of SSA in analyzing hydrometeorological time series (e.g. rainfall, discharge, temperature) can be found in Hanson et al [17] and Marques et al [18]. This method can be used particularly to extract the main components of rainfall and discharge series and to provide good forecast for them [18].…”

Section: Introductionmentioning

confidence: 99%

Coupling Singular Spectrum Analysis with Artificial Neural Network to Improve Accuracy of Sediment Load Prediction

Heng¹,

Suetsugi²

2013

JWARP

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Sediment load estimation is generally required for study and development of water resources system. In this regard, artificial neural network (ANN) is the most widely used modeling tool especially in data-constraint regions. This research attempts to combine SSA (singular spectrum analysis) with ANN, hereafter called SSA-ANN model, with expectation to improve the accuracy of sediment load predicted by the existing ANN approach. Two different catchments located in the Lower Mekong Basin (LMB) were selected for the study and the model performance was measured by several statistical indices. In comparing with ANN, the proposed SSA-ANN model shows its better performance repeatedly in both catchments. In validation stage, SSA-ANN is superior for larger Nash-Sutcliffe Efficiency about 24% in Ban Nong Kiang catchment and 7% in Nam Mae Pun Luang catchment. Other statistical measures of SSA-ANN are better than those of ANN as well. This improvement reveals the importance of SSA which filters noise containing in the raw time series and transforms the original input data to be near normal distribution which is favorable to model simulation. This coupled model is also recommended for the prediction of other water resources variables because extra input data are not required. Only additional computation, time series decomposition, is needed. The proposed technique could be potentially used to minimize the costly operation of sediment measurement in the LMB which is relatively rich in hydrometeorological records.

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“…В последнее время были разработаны новые модели для прогнозирования, основанные на SSA. В [12] предложили линейную рекуррентную формулу син-гулярного спектрального анализа (SSA-LRF), и эта модель была использована для решения некоторых практических задач [13,14].…”

Section: анализ литературных данных и постановка проблемыunclassified

Hybrid mathematical models and methods of time series forecasting taking into account external factors

Щелкалин¹

2014

EEJET

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Singular spectrum analysis and forecasting of hydrological time series

Cited by 96 publications

References 31 publications

Comparison of Two Entropy Spectral Analysis Methods for Streamflow Forecasting in Northwest China

Comparison of Two Entropy Spectral Analysis Methods for Streamflow Forecasting in Northwest China

Coupling Singular Spectrum Analysis with Artificial Neural Network to Improve Accuracy of Sediment Load Prediction

Hybrid mathematical models and methods of time series forecasting taking into account external factors

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