A New Method of Periods' Identification in Hydrologic Series Based on EEMD
Identification of dominant periods is a very important but difficult task in hydrologic time series data analysis. In this paper, for improving the results of periods' identification, a new method, called EEMD-MESA (ensemble empirical mode decomposition-maximum entropy spectral analysis), has b...
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| creator | Yan-Fang Sang Dong Wang Ji-Chun Wu Qing-Ping Zhu Ling Wang |
| description | Identification of dominant periods is a very important but difficult task in hydrologic time series data analysis. In this paper, for improving the results of periods' identification, a new method, called EEMD-MESA (ensemble empirical mode decomposition-maximum entropy spectral analysis), has been proposed, whose main idea is identifying the main intrinsic mode functions (MIMFs) in hydrologic series firstly, and then by using MESA to identify periods in each MIMFs, all periods in the hydrologic series can be gotten finally. By applying to an observed runoff series, advantages of the new method have been verified. Analyses results show that EEMD-MESA is as better as MSSA but much better than other methods (FFT and MESA); While compared with MSSA, EEMD-MESA is more convenient and time-saving. Therefore, the EEMD-MESA method would be more applicable to practical hydrologic works. |
| doi_str_mv | 10.1109/AICI.2009.236 |
| format | Conference Proceeding |
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In this paper, for improving the results of periods' identification, a new method, called EEMD-MESA (ensemble empirical mode decomposition-maximum entropy spectral analysis), has been proposed, whose main idea is identifying the main intrinsic mode functions (MIMFs) in hydrologic series firstly, and then by using MESA to identify periods in each MIMFs, all periods in the hydrologic series can be gotten finally. By applying to an observed runoff series, advantages of the new method have been verified. Analyses results show that EEMD-MESA is as better as MSSA but much better than other methods (FFT and MESA); While compared with MSSA, EEMD-MESA is more convenient and time-saving. Therefore, the EEMD-MESA method would be more applicable to practical hydrologic works.</description><identifier>ISBN: 1424438357</identifier><identifier>ISBN: 9781424438358</identifier><identifier>EISBN: 0769538169</identifier><identifier>EISBN: 9780769538167</identifier><identifier>DOI: 10.1109/AICI.2009.236</identifier><identifier>LCCN: 2009938339</identifier><language>eng</language><publisher>IEEE</publisher><subject>Data analysis ; Data engineering ; Entropy ; Hydrology ; Independent component analysis ; Noise reduction ; Spectral analysis ; Time series analysis ; Water resources ; Wavelet analysis</subject><ispartof>2009 International Conference on Artificial Intelligence and Computational Intelligence, 2009, Vol.4, p.269-273</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5376358$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5376358$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yan-Fang Sang</creatorcontrib><creatorcontrib>Dong Wang</creatorcontrib><creatorcontrib>Ji-Chun Wu</creatorcontrib><creatorcontrib>Qing-Ping Zhu</creatorcontrib><creatorcontrib>Ling Wang</creatorcontrib><title>A New Method of Periods' Identification in Hydrologic Series Based on EEMD</title><title>2009 International Conference on Artificial Intelligence and Computational Intelligence</title><addtitle>AICI</addtitle><description>Identification of dominant periods is a very important but difficult task in hydrologic time series data analysis. 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Therefore, the EEMD-MESA method would be more applicable to practical hydrologic works.</description><subject>Data analysis</subject><subject>Data engineering</subject><subject>Entropy</subject><subject>Hydrology</subject><subject>Independent component analysis</subject><subject>Noise reduction</subject><subject>Spectral analysis</subject><subject>Time series analysis</subject><subject>Water resources</subject><subject>Wavelet analysis</subject><isbn>1424438357</isbn><isbn>9781424438358</isbn><isbn>0769538169</isbn><isbn>9780769538167</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjD1PwzAURY1QJUjpyMTijSnB9vPnGEKgQS0gAXPlJM9gVBKUREL99wTBdHV0z72EnHOWcc7cVV4VVSYYc5kAfUQSZrRTYLl2xyThUkgJFpRZkORXcjOAOyGrcfxgjHGjJQg4Jfc5fcBvusXpvW9pH-gTDrFvx0tatdhNMcTGT7HvaOzo-tAO_b5_iw19ni0c6bUfcV51tCy3N2dkEfx-xNV_LsnrbflSrNPN411V5Js0cqOmNHgBTriANcdQAzrPaw61hcBs0-oGQJgGpG_RMoVWKZwLJZWWnqERDpbk4u83IuLua4iffjjsFBgNysIP2vVM_A</recordid><startdate>200911</startdate><enddate>200911</enddate><creator>Yan-Fang Sang</creator><creator>Dong Wang</creator><creator>Ji-Chun Wu</creator><creator>Qing-Ping Zhu</creator><creator>Ling Wang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200911</creationdate><title>A New Method of Periods' Identification in Hydrologic Series Based on EEMD</title><author>Yan-Fang Sang ; Dong Wang ; Ji-Chun Wu ; Qing-Ping Zhu ; Ling Wang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-fa23929feb1efb3e9a1b13b83f08cd6c3327c34ade805e855ef0854564a0e7293</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Data analysis</topic><topic>Data engineering</topic><topic>Entropy</topic><topic>Hydrology</topic><topic>Independent component analysis</topic><topic>Noise reduction</topic><topic>Spectral analysis</topic><topic>Time series analysis</topic><topic>Water resources</topic><topic>Wavelet analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Yan-Fang Sang</creatorcontrib><creatorcontrib>Dong Wang</creatorcontrib><creatorcontrib>Ji-Chun Wu</creatorcontrib><creatorcontrib>Qing-Ping Zhu</creatorcontrib><creatorcontrib>Ling Wang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yan-Fang Sang</au><au>Dong Wang</au><au>Ji-Chun Wu</au><au>Qing-Ping Zhu</au><au>Ling Wang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A New Method of Periods' Identification in Hydrologic Series Based on EEMD</atitle><btitle>2009 International Conference on Artificial Intelligence and Computational Intelligence</btitle><stitle>AICI</stitle><date>2009-11</date><risdate>2009</risdate><volume>4</volume><spage>269</spage><epage>273</epage><pages>269-273</pages><isbn>1424438357</isbn><isbn>9781424438358</isbn><eisbn>0769538169</eisbn><eisbn>9780769538167</eisbn><abstract>Identification of dominant periods is a very important but difficult task in hydrologic time series data analysis. In this paper, for improving the results of periods' identification, a new method, called EEMD-MESA (ensemble empirical mode decomposition-maximum entropy spectral analysis), has been proposed, whose main idea is identifying the main intrinsic mode functions (MIMFs) in hydrologic series firstly, and then by using MESA to identify periods in each MIMFs, all periods in the hydrologic series can be gotten finally. By applying to an observed runoff series, advantages of the new method have been verified. Analyses results show that EEMD-MESA is as better as MSSA but much better than other methods (FFT and MESA); While compared with MSSA, EEMD-MESA is more convenient and time-saving. Therefore, the EEMD-MESA method would be more applicable to practical hydrologic works.</abstract><pub>IEEE</pub><doi>10.1109/AICI.2009.236</doi><tpages>5</tpages></addata></record> |
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| subjects | Data analysis Data engineering Entropy Hydrology Independent component analysis Noise reduction Spectral analysis Time series analysis Water resources Wavelet analysis |
| title | A New Method of Periods' Identification in Hydrologic Series Based on EEMD |
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