Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin
The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mech...
Gespeichert in:
| Veröffentlicht in: | Hydrological sciences journal 2018-09, Vol.63 (12), p.1817-1830 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1830 |
|---|---|
| container_issue | 12 |
| container_start_page | 1817 |
| container_title | Hydrological sciences journal |
| container_volume | 63 |
| creator | Jiang, Zhiqiang Li, Rongbo Ji, Changming Li, Anqiang Zhou, Jianzhong |
| description | The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application. |
| doi_str_mv | 10.1080/02626667.2018.1541091 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_infor</sourceid><recordid>TN_cdi_proquest_journals_2141100396</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2141100396</sourcerecordid><originalsourceid>FETCH-LOGICAL-c338t-15e58c298cbde5fee93b9e7ee2fd5204d3ef9f70cb678b1e8c505ae6a0ead42a3</originalsourceid><addsrcrecordid>eNp9kE1r3DAQhkVpodttfkJAkLM3-rC89i0htElhoZeW5ibG8mirxSs5ktyy5_zxyt3ttZcRDO8z4n0IueZsw1nLbploRNM0241gvN1wVXPW8TdkJbhilaylektWS6ZaQu_Jh5QOjMm6a-SKvP6AXzhipuBhPCWXqh4SDnSK4YAmu-DpNMc0u5KYc4i4j5jSsj6GAceCDdTlRGGaRmfgL-A8zT-RxtkHa6ktkIGUnd_TYOnO0WcHfn9YBi2fOf-RvLMwJry6vGvy_fOnbw9P1e7r45eH-11lpGxzxRWq1oiuNf2AyiJ2su9wiyjsoASrB4m2s1tm-mbb9hxbo5gCbIAhDLUAuSY357ul3MuMKetDmGPpnbTgNedFSnGyJuqcMjGkFNHqKbojxJPmTC--9T_fevGtL74Ld3fmnC-Vj_A7xHHQGU5jiDaCNy5p-f8TfwB2qIr_</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2141100396</pqid></control><display><type>article</type><title>Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin</title><source>Access via Taylor & Francis</source><source>Alma/SFX Local Collection</source><creator>Jiang, Zhiqiang ; Li, Rongbo ; Ji, Changming ; Li, Anqiang ; Zhou, Jianzhong</creator><creatorcontrib>Jiang, Zhiqiang ; Li, Rongbo ; Ji, Changming ; Li, Anqiang ; Zhou, Jianzhong</creatorcontrib><description>The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application.</description><identifier>ISSN: 0262-6667</identifier><identifier>EISSN: 2150-3435</identifier><identifier>DOI: 10.1080/02626667.2018.1541091</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis</publisher><subject>Accuracy ; Analysis ; auto regression model ; Back propagation networks ; Evaluation ; Forecasting ; Hydroelectric power ; Li Xiangjiang basin ; Mathematical models ; Model accuracy ; Neural networks ; Performance indices ; Peroxisome proliferator-activated receptors ; projection pursuit ; Regression analysis ; Runoff ; Runoff forecasting ; Stability ; Stability analysis ; Wavelet analysis ; Ya Yangshan Reservoir</subject><ispartof>Hydrological sciences journal, 2018-09, Vol.63 (12), p.1817-1830</ispartof><rights>2018 IAHS 2018</rights><rights>2018 IAHS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-15e58c298cbde5fee93b9e7ee2fd5204d3ef9f70cb678b1e8c505ae6a0ead42a3</citedby><cites>FETCH-LOGICAL-c338t-15e58c298cbde5fee93b9e7ee2fd5204d3ef9f70cb678b1e8c505ae6a0ead42a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/02626667.2018.1541091$$EPDF$$P50$$Ginformaworld$$H</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/02626667.2018.1541091$$EHTML$$P50$$Ginformaworld$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,59652,60441</link.rule.ids></links><search><creatorcontrib>Jiang, Zhiqiang</creatorcontrib><creatorcontrib>Li, Rongbo</creatorcontrib><creatorcontrib>Ji, Changming</creatorcontrib><creatorcontrib>Li, Anqiang</creatorcontrib><creatorcontrib>Zhou, Jianzhong</creatorcontrib><title>Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin</title><title>Hydrological sciences journal</title><description>The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application.</description><subject>Accuracy</subject><subject>Analysis</subject><subject>auto regression model</subject><subject>Back propagation networks</subject><subject>Evaluation</subject><subject>Forecasting</subject><subject>Hydroelectric power</subject><subject>Li Xiangjiang basin</subject><subject>Mathematical models</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Performance indices</subject><subject>Peroxisome proliferator-activated receptors</subject><subject>projection pursuit</subject><subject>Regression analysis</subject><subject>Runoff</subject><subject>Runoff forecasting</subject><subject>Stability</subject><subject>Stability analysis</subject><subject>Wavelet analysis</subject><subject>Ya Yangshan Reservoir</subject><issn>0262-6667</issn><issn>2150-3435</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kE1r3DAQhkVpodttfkJAkLM3-rC89i0htElhoZeW5ibG8mirxSs5ktyy5_zxyt3ttZcRDO8z4n0IueZsw1nLbploRNM0241gvN1wVXPW8TdkJbhilaylektWS6ZaQu_Jh5QOjMm6a-SKvP6AXzhipuBhPCWXqh4SDnSK4YAmu-DpNMc0u5KYc4i4j5jSsj6GAceCDdTlRGGaRmfgL-A8zT-RxtkHa6ktkIGUnd_TYOnO0WcHfn9YBi2fOf-RvLMwJry6vGvy_fOnbw9P1e7r45eH-11lpGxzxRWq1oiuNf2AyiJ2su9wiyjsoASrB4m2s1tm-mbb9hxbo5gCbIAhDLUAuSY357ul3MuMKetDmGPpnbTgNedFSnGyJuqcMjGkFNHqKbojxJPmTC--9T_fevGtL74Ld3fmnC-Vj_A7xHHQGU5jiDaCNy5p-f8TfwB2qIr_</recordid><startdate>20180910</startdate><enddate>20180910</enddate><creator>Jiang, Zhiqiang</creator><creator>Li, Rongbo</creator><creator>Ji, Changming</creator><creator>Li, Anqiang</creator><creator>Zhou, Jianzhong</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>SOI</scope></search><sort><creationdate>20180910</creationdate><title>Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin</title><author>Jiang, Zhiqiang ; Li, Rongbo ; Ji, Changming ; Li, Anqiang ; Zhou, Jianzhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-15e58c298cbde5fee93b9e7ee2fd5204d3ef9f70cb678b1e8c505ae6a0ead42a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accuracy</topic><topic>Analysis</topic><topic>auto regression model</topic><topic>Back propagation networks</topic><topic>Evaluation</topic><topic>Forecasting</topic><topic>Hydroelectric power</topic><topic>Li Xiangjiang basin</topic><topic>Mathematical models</topic><topic>Model accuracy</topic><topic>Neural networks</topic><topic>Performance indices</topic><topic>Peroxisome proliferator-activated receptors</topic><topic>projection pursuit</topic><topic>Regression analysis</topic><topic>Runoff</topic><topic>Runoff forecasting</topic><topic>Stability</topic><topic>Stability analysis</topic><topic>Wavelet analysis</topic><topic>Ya Yangshan Reservoir</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Zhiqiang</creatorcontrib><creatorcontrib>Li, Rongbo</creatorcontrib><creatorcontrib>Ji, Changming</creatorcontrib><creatorcontrib>Li, Anqiang</creatorcontrib><creatorcontrib>Zhou, Jianzhong</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Hydrological sciences journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Zhiqiang</au><au>Li, Rongbo</au><au>Ji, Changming</au><au>Li, Anqiang</au><au>Zhou, Jianzhong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin</atitle><jtitle>Hydrological sciences journal</jtitle><date>2018-09-10</date><risdate>2018</risdate><volume>63</volume><issue>12</issue><spage>1817</spage><epage>1830</epage><pages>1817-1830</pages><issn>0262-6667</issn><eissn>2150-3435</eissn><abstract>The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application.</abstract><cop>Abingdon</cop><pub>Taylor & Francis</pub><doi>10.1080/02626667.2018.1541091</doi><tpages>14</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0262-6667 |
| ispartof | Hydrological sciences journal, 2018-09, Vol.63 (12), p.1817-1830 |
| issn | 0262-6667 2150-3435 |
| language | eng |
| recordid | cdi_proquest_journals_2141100396 |
| source | Access via Taylor & Francis; Alma/SFX Local Collection |
| subjects | Accuracy Analysis auto regression model Back propagation networks Evaluation Forecasting Hydroelectric power Li Xiangjiang basin Mathematical models Model accuracy Neural networks Performance indices Peroxisome proliferator-activated receptors projection pursuit Regression analysis Runoff Runoff forecasting Stability Stability analysis Wavelet analysis Ya Yangshan Reservoir |
| title | Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-11T10%3A06%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_infor&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Wavelet%20analysis-based%20projection%20pursuit%20autoregression%20model%20and%20its%20application%20in%20the%20runoff%20forecasting%20of%20Li%20Xiangjiang%20basin&rft.jtitle=Hydrological%20sciences%20journal&rft.au=Jiang,%20Zhiqiang&rft.date=2018-09-10&rft.volume=63&rft.issue=12&rft.spage=1817&rft.epage=1830&rft.pages=1817-1830&rft.issn=0262-6667&rft.eissn=2150-3435&rft_id=info:doi/10.1080/02626667.2018.1541091&rft_dat=%3Cproquest_infor%3E2141100396%3C/proquest_infor%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2141100396&rft_id=info:pmid/&rfr_iscdi=true |