Development of a Hybrid Data Driven Model for Hydrological Estimation
High and low stremflow values forecasting is of great importance in field of water resources in order to mitigate the impacts of flood and drought. Most of water resources models deal with the problem of not being flexible for modeling maximum and minimum flows. To overcome that shortcoming, a combi...
Gespeichert in:
| Veröffentlicht in: | Water resources management 2018-09, Vol.32 (11), p.3737-3750 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3750 |
|---|---|
| container_issue | 11 |
| container_start_page | 3737 |
| container_title | Water resources management |
| container_volume | 32 |
| creator | Araghinejad, Shahab Fayaz, Nima Hosseini-Moghari, Seyed-Mohammad |
| description | High and low stremflow values forecasting is of great importance in field of water resources in order to mitigate the impacts of flood and drought. Most of water resources models deal with the problem of not being flexible for modeling maximum and minimum flows. To overcome that shortcoming, a combination of artificial neural network (ANN) models is developed in this study for monthly streamflow forecasting. A probabilistic neural network (PNN) is used to classify each of the input-output patterns and afterward, the classified data are forecasted using a modified multi-layer perceptron (MMLP). In addition, the performance of the MLP and generalized regression neural network (GRNN) in streamflow forecasting are investigated and compared to the proposed method. The findings indicate that the R
2
associated with the suggested model is 46 and 80% higher compared to MLP and GRNN models, respectively. |
| doi_str_mv | 10.1007/s11269-018-2016-3 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2051403623</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2051403623</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-8041b494c8b9dcf22ed4dcb1dacc5189caca4b297740a820a2f005eef9c044ef3</originalsourceid><addsrcrecordid>eNp1kE1LAzEQhoMoWKs_wFvAc3Qmm_3IUdrVChUveg7ZfJQt201NtoX-e7es4MnTHOZ93hkeQu4RHhGgfEqIvJAMsGIcsGDZBZlhXmYMixwuyQwkByZKgdfkJqUtwEhJmJF66Y6uC_ud6wcaPNV0dWpia-lSD5ouY3t0PX0P1nXUhzgubQxd2LRGd7ROQ7vTQxv6W3LldZfc3e-ck6-X-nOxYuuP17fF85qZDIuBVSCwEVKYqpHWeM6dFdY0aLUxOVbSaKNFw2VZCtAVB809QO6clwaEcD6bk4epdx_D98GlQW3DIfbjScUhRwFZwbMxhVPKxJBSdF7t4_hoPCkEdbalJltqtKXOttSZ4ROTxmy_cfGv-X_oB_C5bE8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2051403623</pqid></control><display><type>article</type><title>Development of a Hybrid Data Driven Model for Hydrological Estimation</title><source>SpringerNature Journals</source><creator>Araghinejad, Shahab ; Fayaz, Nima ; Hosseini-Moghari, Seyed-Mohammad</creator><creatorcontrib>Araghinejad, Shahab ; Fayaz, Nima ; Hosseini-Moghari, Seyed-Mohammad</creatorcontrib><description>High and low stremflow values forecasting is of great importance in field of water resources in order to mitigate the impacts of flood and drought. Most of water resources models deal with the problem of not being flexible for modeling maximum and minimum flows. To overcome that shortcoming, a combination of artificial neural network (ANN) models is developed in this study for monthly streamflow forecasting. A probabilistic neural network (PNN) is used to classify each of the input-output patterns and afterward, the classified data are forecasted using a modified multi-layer perceptron (MMLP). In addition, the performance of the MLP and generalized regression neural network (GRNN) in streamflow forecasting are investigated and compared to the proposed method. The findings indicate that the R
2
associated with the suggested model is 46 and 80% higher compared to MLP and GRNN models, respectively.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-018-2016-3</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Artificial neural networks ; Atmospheric Sciences ; Civil Engineering ; Drought ; Earth and Environmental Science ; Earth Sciences ; Environment ; Forecasting ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Hydrologic models ; Hydrology ; Hydrology/Water Resources ; Modelling ; Multilayers ; Neural networks ; Resources ; Statistical analysis ; Stream discharge ; Stream flow ; Streamflow forecasting ; Water resources</subject><ispartof>Water resources management, 2018-09, Vol.32 (11), p.3737-3750</ispartof><rights>Springer Science+Business Media B.V., part of Springer Nature 2018</rights><rights>Water Resources Management is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-8041b494c8b9dcf22ed4dcb1dacc5189caca4b297740a820a2f005eef9c044ef3</citedby><cites>FETCH-LOGICAL-c316t-8041b494c8b9dcf22ed4dcb1dacc5189caca4b297740a820a2f005eef9c044ef3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11269-018-2016-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-018-2016-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Araghinejad, Shahab</creatorcontrib><creatorcontrib>Fayaz, Nima</creatorcontrib><creatorcontrib>Hosseini-Moghari, Seyed-Mohammad</creatorcontrib><title>Development of a Hybrid Data Driven Model for Hydrological Estimation</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>High and low stremflow values forecasting is of great importance in field of water resources in order to mitigate the impacts of flood and drought. Most of water resources models deal with the problem of not being flexible for modeling maximum and minimum flows. To overcome that shortcoming, a combination of artificial neural network (ANN) models is developed in this study for monthly streamflow forecasting. A probabilistic neural network (PNN) is used to classify each of the input-output patterns and afterward, the classified data are forecasted using a modified multi-layer perceptron (MMLP). In addition, the performance of the MLP and generalized regression neural network (GRNN) in streamflow forecasting are investigated and compared to the proposed method. The findings indicate that the R
2
associated with the suggested model is 46 and 80% higher compared to MLP and GRNN models, respectively.</description><subject>Artificial neural networks</subject><subject>Atmospheric Sciences</subject><subject>Civil Engineering</subject><subject>Drought</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environment</subject><subject>Forecasting</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Hydrologic models</subject><subject>Hydrology</subject><subject>Hydrology/Water Resources</subject><subject>Modelling</subject><subject>Multilayers</subject><subject>Neural networks</subject><subject>Resources</subject><subject>Statistical analysis</subject><subject>Stream discharge</subject><subject>Stream flow</subject><subject>Streamflow forecasting</subject><subject>Water resources</subject><issn>0920-4741</issn><issn>1573-1650</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kE1LAzEQhoMoWKs_wFvAc3Qmm_3IUdrVChUveg7ZfJQt201NtoX-e7es4MnTHOZ93hkeQu4RHhGgfEqIvJAMsGIcsGDZBZlhXmYMixwuyQwkByZKgdfkJqUtwEhJmJF66Y6uC_ud6wcaPNV0dWpia-lSD5ouY3t0PX0P1nXUhzgubQxd2LRGd7ROQ7vTQxv6W3LldZfc3e-ck6-X-nOxYuuP17fF85qZDIuBVSCwEVKYqpHWeM6dFdY0aLUxOVbSaKNFw2VZCtAVB809QO6clwaEcD6bk4epdx_D98GlQW3DIfbjScUhRwFZwbMxhVPKxJBSdF7t4_hoPCkEdbalJltqtKXOttSZ4ROTxmy_cfGv-X_oB_C5bE8</recordid><startdate>20180901</startdate><enddate>20180901</enddate><creator>Araghinejad, Shahab</creator><creator>Fayaz, Nima</creator><creator>Hosseini-Moghari, Seyed-Mohammad</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7ST</scope><scope>7UA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>KR7</scope><scope>L.-</scope><scope>L.G</scope><scope>L6V</scope><scope>LK8</scope><scope>M0C</scope><scope>M2P</scope><scope>M7P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>20180901</creationdate><title>Development of a Hybrid Data Driven Model for Hydrological Estimation</title><author>Araghinejad, Shahab ; Fayaz, Nima ; Hosseini-Moghari, Seyed-Mohammad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-8041b494c8b9dcf22ed4dcb1dacc5189caca4b297740a820a2f005eef9c044ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Artificial neural networks</topic><topic>Atmospheric Sciences</topic><topic>Civil Engineering</topic><topic>Drought</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environment</topic><topic>Forecasting</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Hydrogeology</topic><topic>Hydrologic models</topic><topic>Hydrology</topic><topic>Hydrology/Water Resources</topic><topic>Modelling</topic><topic>Multilayers</topic><topic>Neural networks</topic><topic>Resources</topic><topic>Statistical analysis</topic><topic>Stream discharge</topic><topic>Stream flow</topic><topic>Streamflow forecasting</topic><topic>Water resources</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Araghinejad, Shahab</creatorcontrib><creatorcontrib>Fayaz, Nima</creatorcontrib><creatorcontrib>Hosseini-Moghari, Seyed-Mohammad</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ABI/INFORM Professional Advanced</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>ABI/INFORM Global</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Water resources management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Araghinejad, Shahab</au><au>Fayaz, Nima</au><au>Hosseini-Moghari, Seyed-Mohammad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Development of a Hybrid Data Driven Model for Hydrological Estimation</atitle><jtitle>Water resources management</jtitle><stitle>Water Resour Manage</stitle><date>2018-09-01</date><risdate>2018</risdate><volume>32</volume><issue>11</issue><spage>3737</spage><epage>3750</epage><pages>3737-3750</pages><issn>0920-4741</issn><eissn>1573-1650</eissn><abstract>High and low stremflow values forecasting is of great importance in field of water resources in order to mitigate the impacts of flood and drought. Most of water resources models deal with the problem of not being flexible for modeling maximum and minimum flows. To overcome that shortcoming, a combination of artificial neural network (ANN) models is developed in this study for monthly streamflow forecasting. A probabilistic neural network (PNN) is used to classify each of the input-output patterns and afterward, the classified data are forecasted using a modified multi-layer perceptron (MMLP). In addition, the performance of the MLP and generalized regression neural network (GRNN) in streamflow forecasting are investigated and compared to the proposed method. The findings indicate that the R
2
associated with the suggested model is 46 and 80% higher compared to MLP and GRNN models, respectively.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-018-2016-3</doi><tpages>14</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0920-4741 |
| ispartof | Water resources management, 2018-09, Vol.32 (11), p.3737-3750 |
| issn | 0920-4741 1573-1650 |
| language | eng |
| recordid | cdi_proquest_journals_2051403623 |
| source | SpringerNature Journals |
| subjects | Artificial neural networks Atmospheric Sciences Civil Engineering Drought Earth and Environmental Science Earth Sciences Environment Forecasting Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrologic models Hydrology Hydrology/Water Resources Modelling Multilayers Neural networks Resources Statistical analysis Stream discharge Stream flow Streamflow forecasting Water resources |
| title | Development of a Hybrid Data Driven Model for Hydrological Estimation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T23%3A24%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Development%20of%20a%20Hybrid%20Data%20Driven%20Model%20for%20Hydrological%20Estimation&rft.jtitle=Water%20resources%20management&rft.au=Araghinejad,%20Shahab&rft.date=2018-09-01&rft.volume=32&rft.issue=11&rft.spage=3737&rft.epage=3750&rft.pages=3737-3750&rft.issn=0920-4741&rft.eissn=1573-1650&rft_id=info:doi/10.1007/s11269-018-2016-3&rft_dat=%3Cproquest_cross%3E2051403623%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2051403623&rft_id=info:pmid/&rfr_iscdi=true |