Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment

Abstract In this study we have developed a conjunction model, WGP, of discrete wavelet transform (DWT) and genetic programming (GP) for forecasting river floods when the only data available are the historical daily flows. DWT is used for denoising and smoothening the observed flow time series on which GP is implemented to get the next-day flood. The new model is compared with autoregressive (AR) and stand-alone GP models. All models are calibrated and tested on the Kosi River which is one of the most devastating rivers of the world with high and spiky monsoon flows, modeling of which poses a great challenge. With different inputs, 12 models, four in each class of WGP, GP and AR, are devised. The best performing WGP model, WGP4, with four previous daily flow rates as input, forecasts the Kosi floods with an accuracy of 87.9%, root mean square error of 123.9 m3/s and Nash–Sutcliffe coefficient of 0.993, the best performance indices among all the developed models. The extreme floods are also better simulated by the WGP models than by AR and GP models.

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Multiple mechanisms generate Lorentzian and 1/fα power spectra in daily stream-flow time series

Advances in Water Resources ◽

10.1016/j.advwatres.2011.10.010 ◽

2012 ◽

Vol 37 ◽

pp. 94-103 ◽

Cited By ~ 14

Author(s):

Sally E. Thompson ◽

Gabriel G. Katul

Keyword(s):

Time Series ◽

Stream Flow ◽

Power Spectra ◽

Flow Time

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Analysis and prediction of chaos in rainfall and stream flow time series

Journal of Hydrology ◽

10.1016/0022-1694(94)90185-6 ◽

1994 ◽

Vol 153 (1-4) ◽

pp. 23-52 ◽

Cited By ~ 153

Author(s):

A.W. Jayawardena ◽

Feizhou Lai

Keyword(s):

Time Series ◽

Stream Flow ◽

Flow Time

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Analysis of long-term fluctuations in stream flow time series: An application to Litani River, Lebanon

Acta Geophysica ◽

10.2478/s11600-013-0175-4 ◽

2013 ◽

Vol 62 (1) ◽

pp. 164-179 ◽

Cited By ~ 6

Author(s):

Amin Shaban ◽

Luciano Telesca ◽

Talal Darwich ◽

Nabil Amacha

Keyword(s):

Time Series ◽

Stream Flow ◽

Flow Time ◽

Litani River

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Time-frequency analysis of fetal heartbeat fluctuation using wavelet transform

AJP Heart and Circulatory Physiology ◽

10.1152/ajpheart.1998.275.6.h1993 ◽

1998 ◽

Vol 275 (6) ◽

pp. H1993-H1999 ◽

Cited By ~ 5

Author(s):

Yoshitaka Kimura ◽

Kunihiro Okamura ◽

Takanori Watanabe ◽

Nobuo Yaegashi ◽

Shigeki Uehara ◽

...

Keyword(s):

Heart Rate ◽

Time Series ◽

Wavelet Transform ◽

Gamma Distribution ◽

Frequency Analysis ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Time Frequency Analysis ◽

Time Frequency ◽

Rate Fluctuation

We examined whether the nonlinear control mechanism of the fetal autonomic nervous system would change in various fetal states. Eight thousand or more fetal heartbeats were detected from normal, hypoxemic, and acidemic fetuses. Fetal heart Doppler-signal intervals were determined in a high-precision autocorrelation method, and a time series of fetal heart rate fluctuation was obtained. The distribution of the amplitude of temporal fluctuation in the low-frequency component of fetal heart rate frequency was studied using a method of time-frequency analysis called wavelet transform. Spline 4 was used as the mother wavelet function. A gamma distribution was observed from 17 wk of gestation onward. The value of the parameter ν of this gamma distribution was ∼1.6 and remained constant regardless of the gestational age or the time of day. The value of ν decreased significantly to 0.77 when the fetus developed acidemia and was 1.51 in hypoxemia and 1.54 in a normal condition. This study elucidates a nonlinear structure of the time series of heart rate fluctuation of the gamma distribution in the human fetus. This technique may provide a new quantitative index of fetal monitoring to diagnose fetal acidemia.

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A general theory on frequency and time–frequency analysis of irregularly sampled time series based on projection methods – Part 2: Extension to time–frequency analysis

Nonlinear Processes in Geophysics ◽

10.5194/npg-25-175-2018 ◽

2018 ◽

Vol 25 (1) ◽

pp. 175-200 ◽

Cited By ~ 2

Author(s):

Guillaume Lenoir ◽

Michel Crucifix

Keyword(s):

Time Series ◽

Wavelet Transform ◽

Frequency Analysis ◽

Continuous Wavelet Transform ◽

Background Noise ◽

Noise Model ◽

Analysis Tool ◽

Continuous Wavelet ◽

Time Frequency Analysis ◽

Time Frequency

Abstract. Geophysical time series are sometimes sampled irregularly along the time axis. The situation is particularly frequent in palaeoclimatology. Yet, there is so far no general framework for handling the continuous wavelet transform when the time sampling is irregular. Here we provide such a framework. To this end, we define the scalogram as the continuous-wavelet-transform equivalent of the extended Lomb–Scargle periodogram defined in Part 1 of this study (Lenoir and Crucifix, 2018). The signal being analysed is modelled as the sum of a locally periodic component in the time–frequency plane, a polynomial trend, and a background noise. The mother wavelet adopted here is the Morlet wavelet classically used in geophysical applications. The background noise model is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, which is more general than the traditional Gaussian white and red noise processes. The scalogram is smoothed by averaging over neighbouring times in order to reduce its variance. The Shannon–Nyquist exclusion zone is however defined as the area corrupted by local aliasing issues. The local amplitude in the time–frequency plane is then estimated with least-squares methods. We also derive an approximate formula linking the squared amplitude and the scalogram. Based on this property, we define a new analysis tool: the weighted smoothed scalogram, which we recommend for most analyses. The estimated signal amplitude also gives access to band and ridge filtering. Finally, we design a test of significance for the weighted smoothed scalogram against the stationary Gaussian CARMA background noise, and provide algorithms for computing confidence levels, either analytically or with Monte Carlo Markov chain methods. All the analysis tools presented in this article are available to the reader in the Python package WAVEPAL.

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Changes in land cover and stream flows in Gilgel Abbay catchment, Upper Blue Nile basin – Ethiopia

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-7-9567-2010 ◽

2010 ◽

Vol 7 (6) ◽

pp. 9567-9598 ◽

Cited By ~ 4

Author(s):

T. H. M. Rientjes ◽

A. T. Haile ◽

C. M. M. Mannaerts ◽

E. Kebede ◽

E. Habib

Keyword(s):

Time Series ◽

Land Cover ◽

Stream Flow ◽

Agricultural Land ◽

Flow Time ◽

Nile Basin ◽

Classification Analysis ◽

Blue Nile ◽

Blue Nile Basin ◽

Upper Blue Nile Basin

Abstract. We evaluated the land cover change in the Upper Gilgel Abbay catchment in the Upper Blue Nile basin through classification analysis of remote sensing based land cover data and through assessing the changes in the hydrological regime by statistical analysis of stream flow observations. Results of the land cover classification analysis indicated that 50.9% and 16.7% of the catchment area was covered by forest in 1973 and 2001, respectively. This significant decrease in forest cover is mainly due to expansion of agricultural land. A comparison of stream flow time series of the Upper Gilgel Abbay catchment to stream flow time series from two neighbouring catchments shows a different trend and a statistically significant change over time. In 1986–2001, the annual and the high flows of the catchment increased by 13% and 46%, respectively while the low flows decreased by 35%. Generally, the results indicate significant changes in land cover and the hydrological regimes of the Upper Gilgel Abbay catchment over the past 30 years.

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