Sample entropy‐based adaptive wavelet de‐noising approach for meteorologic and hydrologic time series

Wang, Dong; Singh, Vijay P.; Shang, Xiaosan; Ding, Hao; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi; Wang, Shicheng; Wang, Zhenlong

doi:10.1002/2014jd021869

Cited by 21 publications

(19 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wavelet denoising based on the thresholds introduced by Donoho et al (1995) has been applied to hydrometeorological analysis (Wang et al, 2005(Wang et al, , 2014Chou, 2011). In this study, the following three analysis steps were performed for denoising the time series data.…”

Section: Wavelet Denoisingmentioning

confidence: 99%

“…A soft or hard threshold can be used to deal with detail coefficients and obtain the decomposed coefficient. In this study, a soft threshold was selected because it performed better than a hard threshold (Wang et al, 2014;Chou, 2011).…”

Section: Wavelet Denoisingmentioning

confidence: 99%

“…For WBCs such as surplus and deficit, the performance of the ARIMA model was similar to that of the A ET , except for a few cases. Because hydrometeorological data are affected by noises from different hydrophysical processes (Wang et al, 2014), the results obtained using the ARIMA models were unsatisfactory. To improve model performance, noise must be removed from the data.…”

Section: Model Selection and Forecasting Abilitymentioning

confidence: 99%

“…In this study, DWT denoising was applied to the WBC data and the quality of the denoising time series data was examined before further processing. When selecting a method for denoising the time series using WT, the mean of the original and denoising time series data should be close and the standard deviation of the denoising time series should be less than that of the original time series (Wang et al, 2014). Figure 5a displays the means of the actual and wavelet denoising P ET time series.…”

Section: Model Selection and Forecasting Abilitymentioning

confidence: 99%

“…However, hydrometeorological time series are con-4214 A. T. M. S. Rahman et al: Modeling the changes in water balance components taminated by noises from hydrophysical processes. This affects the accuracy of the analysis, simulation, and forecasting Wang et al, 2014). Hence, denoising the time series is essential for improving the accuracy of the obtained results.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Rahman

Ahmed

Adnan

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. The objectives of the present study were to explore the changes in the water balance components (WBCs) by co-utilizing the discrete wavelet transform (DWT) and different forms of the Mann-Kendall (MK) test and develop a wavelet denoise autoregressive integrated moving average (WD-ARIMA) model for forecasting the WBCs. The results revealed that most of the potential evapotranspiration (P ET ) trends (approximately 73 %) had a decreasing tendency from 1981-1982 to 2012-2013 in the western part of Bangladesh. However, most of the trends (approximately 82 %) were not statistically significant at a 5 % significance level. The actual evapotranspiration (A ET ), annual deficit, and annual surplus also exhibited a similar tendency. The rainfall and temperature exhibited increasing trends. However, the WBCs exhibited an inverse trend, which suggested that the P ET changes associated with temperature changes could not explain the change in the WBCs. Moreover, the 8-year (D3) and 16-year (D4) periodic components were generally responsible for the trends found in the original WBC data for the study area. The actual data was affected by noise, which resulted in the ARIMA model exhibiting an unsatisfactory performance. Therefore, wavelet denoising of the WBC time series was conducted to improve the performance of the ARIMA model. The quality of the denoising time series data was ensured using relevant statistical analysis. The performance of the WD-ARIMA model was assessed using the Nash-Sutcliffe efficiency (NSE) coefficient and coefficient of determination (R 2 ). The WD-ARIMA model exhibited very good performance, which clearly demonstrated the advantages of denoising the time series data for forecasting the WBCs. The validation results of the model revealed that the forecasted values were very close to actual values, with an acceptable mean percentage error. The residuals also followed a normal distribution. The performance and validation results indicated that models can be used for the short-term forecasting of WBCs. Further studies on different combinations of wavelet analysis are required to develop a superior model for the hydrological forecasting in the context of climate change. The findings of this study can be used to improve water resource management in the highly irrigated western part of Bangladesh.

show abstract

Section: Wavelet Denoisingmentioning

confidence: 99%

Section: Wavelet Denoisingmentioning

confidence: 99%

Section: Model Selection and Forecasting Abilitymentioning

confidence: 99%

Section: Model Selection and Forecasting Abilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Rahman

Ahmed

Adnan

et al. 2018

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

A hybrid wavelet analysis–cloud model data‐extending approach for meteorologic and hydrologic time series

et al. 2015

Self Cite

View full text Add to dashboard Cite

For scientific and sustainable management of water resources, hydrologic and meteorologic data series need to be often extended. This paper proposes a hybrid approach, named WA-CM (wavelet analysis-cloud model), for data series extension. Wavelet analysis has time-frequency localization features, known as "mathematics microscope," that can decompose and reconstruct hydrologic and meteorologic series by wavelet transform. The cloud model is a mathematical representation of fuzziness and randomness and has strong robustness for uncertain data. The WA-CM approach first employs the wavelet transform to decompose the measured nonstationary series and then uses the cloud model to develop an extension model for each decomposition layer series. The final extension is obtained by summing the results of extension of each layer. Two kinds of meteorologic and hydrologic data sets with different characteristics and different influence of human activity from six (three pairs) representative stations are used to illustrate the WA-CM approach. The approach is also compared with four other methods, which are conventional correlation extension method, Kendall-Theil robust line method, artificial neural network method (back propagation, multilayer perceptron, and radial basis function), and single cloud model method. To evaluate the model performance completely and thoroughly, five measures are used, which are relative error, mean relative error, standard deviation of relative error, root mean square error, and Thiel inequality coefficient. Results show that the WA-CM approach is effective, feasible, and accurate and is found to be better than other four methods compared. The theory employed and the approach developed here can be applied to extension of data in other areas as well.

show abstract

Wavelet analysis of precipitation extremes over Canadian ecoregions and teleconnections to large‐scale climate anomalies

2016

View full text Add to dashboard Cite

To detect significant interannual and interdecadal oscillations and their teleconnections to large‐scale climate anomalies such as El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO), monthly and seasonal maximum daily precipitation (MMDP and SMDP) from 131 stations across Canada were analyzed by using variants of wavelet analysis. Interannual (1–8 years) oscillations were found to be more significant than interdecadal (8–30 years) oscillations for all selected stations, and the oscillations are both spatial and time‐dependent. Similarly, the significant wavelet coherence and the phase difference between leading principal components of monthly precipitation extremes and climate indices were highly variable in time and in periodicity, and a single climate index explains less than 40% of the total variability. Partial wavelet coherence analysis shows that both ENSO and PDO modulated the interannual variability and PDO modulated the interdecadal variability, of MMDP over Canada. NAO is correlated with the western MMDP at interdecadal scale and the eastern MMDP at interannual scale. The composite analysis shows that precipitation extremes at about three fourths of the stations have been significantly influenced by ENSO and PDO patterns, while about one half of the stations by the NAO patterns. The magnitude of SMDP in extreme El Niño years, and extreme PDO event of positive phase, was mostly lower (higher) over the Canadian Prairies in summer and winter (spring and autumn) than in extreme La Niña years. Overall, the degree of influence of large‐scale climate patterns on Canadian precipitation extremes varies by season and by region.

show abstract

Sample entropy‐based adaptive wavelet de‐noising approach for meteorologic and hydrologic time series

Cited by 21 publications

References 45 publications

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

Modeling the changes in water balance components of the highly irrigated western part of Bangladesh

A hybrid wavelet analysis–cloud model data‐extending approach for meteorologic and hydrologic time series

Wavelet analysis of precipitation extremes over Canadian ecoregions and teleconnections to large‐scale climate anomalies

Contact Info

Product

Resources

About