Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment
Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), whic...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chong, Kai Lun [verfasserIn] Lai, Sai Hin [verfasserIn] El-Shafie, Ahmed [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Water resources management - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 33(2019), 6 vom: Apr., Seite 2015-2032 |
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| Übergeordnetes Werk: |
volume:33 ; year:2019 ; number:6 ; month:04 ; pages:2015-2032 |
| Links: |
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| DOI / URN: |
10.1007/s11269-019-02226-7 |
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| Katalog-ID: |
SPR018401384 |
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| 520 | |a Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. | ||
| 650 | 4 | |a Wavelet transform |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Fourier transform |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Mann-Kendall test |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Trends |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sequential Mann-Kendall test |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spearman’ rho test |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Lai, Sai Hin |e verfasserin |4 aut | |
| 700 | 1 | |a El-Shafie, Ahmed |e verfasserin |4 aut | |
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10.1007/s11269-019-02226-7 doi (DE-627)SPR018401384 (SPR)s11269-019-02226-7-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Chong, Kai Lun verfasserin aut Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. Wavelet transform (dpeaa)DE-He213 Fourier transform (dpeaa)DE-He213 Mann-Kendall test (dpeaa)DE-He213 Trends (dpeaa)DE-He213 Sequential Mann-Kendall test (dpeaa)DE-He213 Spearman’ rho test (dpeaa)DE-He213 Lai, Sai Hin verfasserin aut El-Shafie, Ahmed verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 6 vom: Apr., Seite 2015-2032 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:6 month:04 pages:2015-2032 https://dx.doi.org/10.1007/s11269-019-02226-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 6 04 2015-2032 |
| spelling |
10.1007/s11269-019-02226-7 doi (DE-627)SPR018401384 (SPR)s11269-019-02226-7-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Chong, Kai Lun verfasserin aut Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. Wavelet transform (dpeaa)DE-He213 Fourier transform (dpeaa)DE-He213 Mann-Kendall test (dpeaa)DE-He213 Trends (dpeaa)DE-He213 Sequential Mann-Kendall test (dpeaa)DE-He213 Spearman’ rho test (dpeaa)DE-He213 Lai, Sai Hin verfasserin aut El-Shafie, Ahmed verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 6 vom: Apr., Seite 2015-2032 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:6 month:04 pages:2015-2032 https://dx.doi.org/10.1007/s11269-019-02226-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 6 04 2015-2032 |
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10.1007/s11269-019-02226-7 doi (DE-627)SPR018401384 (SPR)s11269-019-02226-7-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Chong, Kai Lun verfasserin aut Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. Wavelet transform (dpeaa)DE-He213 Fourier transform (dpeaa)DE-He213 Mann-Kendall test (dpeaa)DE-He213 Trends (dpeaa)DE-He213 Sequential Mann-Kendall test (dpeaa)DE-He213 Spearman’ rho test (dpeaa)DE-He213 Lai, Sai Hin verfasserin aut El-Shafie, Ahmed verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 6 vom: Apr., Seite 2015-2032 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:6 month:04 pages:2015-2032 https://dx.doi.org/10.1007/s11269-019-02226-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 6 04 2015-2032 |
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10.1007/s11269-019-02226-7 doi (DE-627)SPR018401384 (SPR)s11269-019-02226-7-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Chong, Kai Lun verfasserin aut Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. Wavelet transform (dpeaa)DE-He213 Fourier transform (dpeaa)DE-He213 Mann-Kendall test (dpeaa)DE-He213 Trends (dpeaa)DE-He213 Sequential Mann-Kendall test (dpeaa)DE-He213 Spearman’ rho test (dpeaa)DE-He213 Lai, Sai Hin verfasserin aut El-Shafie, Ahmed verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 6 vom: Apr., Seite 2015-2032 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:6 month:04 pages:2015-2032 https://dx.doi.org/10.1007/s11269-019-02226-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 6 04 2015-2032 |
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10.1007/s11269-019-02226-7 doi (DE-627)SPR018401384 (SPR)s11269-019-02226-7-e DE-627 ger DE-627 rakwb eng 550 630 ASE 43.33 bkl Chong, Kai Lun verfasserin aut Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. Wavelet transform (dpeaa)DE-He213 Fourier transform (dpeaa)DE-He213 Mann-Kendall test (dpeaa)DE-He213 Trends (dpeaa)DE-He213 Sequential Mann-Kendall test (dpeaa)DE-He213 Spearman’ rho test (dpeaa)DE-He213 Lai, Sai Hin verfasserin aut El-Shafie, Ahmed verfasserin aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 33(2019), 6 vom: Apr., Seite 2015-2032 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:33 year:2019 number:6 month:04 pages:2015-2032 https://dx.doi.org/10.1007/s11269-019-02226-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 43.33 ASE AR 33 2019 6 04 2015-2032 |
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Enthalten in Water resources management 33(2019), 6 vom: Apr., Seite 2015-2032 volume:33 year:2019 number:6 month:04 pages:2015-2032 |
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Chong, Kai Lun @@aut@@ Lai, Sai Hin @@aut@@ El-Shafie, Ahmed @@aut@@ |
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Chong, Kai Lun |
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Chong, Kai Lun ddc 550 bkl 43.33 misc Wavelet transform misc Fourier transform misc Mann-Kendall test misc Trends misc Sequential Mann-Kendall test misc Spearman’ rho test Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment |
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550 630 ASE 43.33 bkl Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment Wavelet transform (dpeaa)DE-He213 Fourier transform (dpeaa)DE-He213 Mann-Kendall test (dpeaa)DE-He213 Trends (dpeaa)DE-He213 Sequential Mann-Kendall test (dpeaa)DE-He213 Spearman’ rho test (dpeaa)DE-He213 |
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ddc 550 bkl 43.33 misc Wavelet transform misc Fourier transform misc Mann-Kendall test misc Trends misc Sequential Mann-Kendall test misc Spearman’ rho test |
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Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment |
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Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment |
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wavelet transform based method for river stream flow time series frequency analysis and assessment in tropical environment |
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Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment |
| abstract |
Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. |
| abstractGer |
Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. |
| abstract_unstemmed |
Abstract The main aim of this study is to perform a time series frequency analysis and assessment for stream flow over the Johor River using a comparative method between wavelet transform (WT) and Fourier transform (FT). One of the wavelet analyses used was the discrete wavelet transform (DWT), which revealed the periodic wavelet components responsible for the trend detection. Using the FT, the periodicities that governed the trend can be obtained; however, in terms of the time domain analysis, FT seems to be lacking compared to the WT. The conditions for using DWT are discussed, and the selection decisions for such discretization are considered. Besides, using the global wavelet spectrum (GWS) and the continuous wavelet transform (CWT), the dominant periodicity components can be further well described in time frequency characteristic. In addition, the integration of the WT and Mann–Kendall (MK) test allows the determination of possible trends present in the stream flow dataset series. It is shown that the wavelet analysis is more suitable than the Fourier analysis as it exhibits good extraction of the time and frequency characteristics, especially for a nonstationary data series. |
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Wavelet Transform Based Method for River Stream Flow Time Series Frequency Analysis and Assessment in Tropical Environment |
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https://dx.doi.org/10.1007/s11269-019-02226-7 |
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Lai, Sai Hin El-Shafie, Ahmed |
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| doi_str |
10.1007/s11269-019-02226-7 |
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2024-07-03T19:25:41.129Z |
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| score |
7.402916 |