Artificial neural network modelling and flood water level prediction using extended Kalman filter
Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (...
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 538 |
|---|---|
| container_issue | |
| container_start_page | 535 |
| container_title | |
| container_volume | |
| creator | Adnan, R. Ruslan, F. A. Samad, A. M. Zain, Z. M. |
| description | Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of flood water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the flood location and three upstream locations of river water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero. |
| doi_str_mv | 10.1109/ICCSCE.2012.6487204 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6487204</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6487204</ieee_id><sourcerecordid>6487204</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-23d8be5ea457620b5c803591b4ce8fe2821441c5cbbb564ce2804c2de631958d3</originalsourceid><addsrcrecordid>eNo1kEtOwzAYhI0QElBygm58gQa_4yyrqEBFJRbAunLsP8jgOJXjUrg9KZTVaEbfzGIQmlNSUkrq23XTPDerkhHKSiV0xYg4Q9dUqIpzKqg4R0Vd6X_P5CUqxvGdEDK1lSbyCpllyr7z1puAI-zTr-TDkD5wPzgIwcc3bKLDXRgGhw8mQ8IBPiHgXQLnbfZDxPvxiMFXhujA4UcTehNx58NE36CLzoQRipPO0Ovd6qV5WGye7tfNcrPwtJJ5wbjTLUgwQlaKkVZaTbisaSss6A6YZlQIaqVt21aqKWSaCMscKE5rqR2fofnfrgeA7S753qTv7ekV_gPOcVdt</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Artificial neural network modelling and flood water level prediction using extended Kalman filter</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Adnan, R. ; Ruslan, F. A. ; Samad, A. M. ; Zain, Z. M.</creator><creatorcontrib>Adnan, R. ; Ruslan, F. A. ; Samad, A. M. ; Zain, Z. M.</creatorcontrib><description>Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of flood water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the flood location and three upstream locations of river water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.</description><identifier>ISBN: 9781467331425</identifier><identifier>ISBN: 1467331422</identifier><identifier>EISBN: 1467331414</identifier><identifier>EISBN: 1467331430</identifier><identifier>EISBN: 9781467331432</identifier><identifier>EISBN: 9781467331418</identifier><identifier>DOI: 10.1109/ICCSCE.2012.6487204</identifier><language>eng</language><publisher>IEEE</publisher><subject>Back Propagation Neural Network (BPN) ; Extended Kalman Filter (EKF) ; Flood Modelling and Prediction</subject><ispartof>2012 IEEE International Conference on Control System, Computing and Engineering, 2012, p.535-538</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/6487204$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6487204$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Adnan, R.</creatorcontrib><creatorcontrib>Ruslan, F. A.</creatorcontrib><creatorcontrib>Samad, A. M.</creatorcontrib><creatorcontrib>Zain, Z. M.</creatorcontrib><title>Artificial neural network modelling and flood water level prediction using extended Kalman filter</title><title>2012 IEEE International Conference on Control System, Computing and Engineering</title><addtitle>ICCSCE</addtitle><description>Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of flood water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the flood location and three upstream locations of river water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.</description><subject>Back Propagation Neural Network (BPN)</subject><subject>Extended Kalman Filter (EKF)</subject><subject>Flood Modelling and Prediction</subject><isbn>9781467331425</isbn><isbn>1467331422</isbn><isbn>1467331414</isbn><isbn>1467331430</isbn><isbn>9781467331432</isbn><isbn>9781467331418</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kEtOwzAYhI0QElBygm58gQa_4yyrqEBFJRbAunLsP8jgOJXjUrg9KZTVaEbfzGIQmlNSUkrq23XTPDerkhHKSiV0xYg4Q9dUqIpzKqg4R0Vd6X_P5CUqxvGdEDK1lSbyCpllyr7z1puAI-zTr-TDkD5wPzgIwcc3bKLDXRgGhw8mQ8IBPiHgXQLnbfZDxPvxiMFXhujA4UcTehNx58NE36CLzoQRipPO0Ovd6qV5WGye7tfNcrPwtJJ5wbjTLUgwQlaKkVZaTbisaSss6A6YZlQIaqVt21aqKWSaCMscKE5rqR2fofnfrgeA7S753qTv7ekV_gPOcVdt</recordid><startdate>201211</startdate><enddate>201211</enddate><creator>Adnan, R.</creator><creator>Ruslan, F. A.</creator><creator>Samad, A. M.</creator><creator>Zain, Z. M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201211</creationdate><title>Artificial neural network modelling and flood water level prediction using extended Kalman filter</title><author>Adnan, R. ; Ruslan, F. A. ; Samad, A. M. ; Zain, Z. M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-23d8be5ea457620b5c803591b4ce8fe2821441c5cbbb564ce2804c2de631958d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Back Propagation Neural Network (BPN)</topic><topic>Extended Kalman Filter (EKF)</topic><topic>Flood Modelling and Prediction</topic><toplevel>online_resources</toplevel><creatorcontrib>Adnan, R.</creatorcontrib><creatorcontrib>Ruslan, F. A.</creatorcontrib><creatorcontrib>Samad, A. M.</creatorcontrib><creatorcontrib>Zain, Z. M.</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/IET 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>Adnan, R.</au><au>Ruslan, F. A.</au><au>Samad, A. M.</au><au>Zain, Z. M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Artificial neural network modelling and flood water level prediction using extended Kalman filter</atitle><btitle>2012 IEEE International Conference on Control System, Computing and Engineering</btitle><stitle>ICCSCE</stitle><date>2012-11</date><risdate>2012</risdate><spage>535</spage><epage>538</epage><pages>535-538</pages><isbn>9781467331425</isbn><isbn>1467331422</isbn><eisbn>1467331414</eisbn><eisbn>1467331430</eisbn><eisbn>9781467331432</eisbn><eisbn>9781467331418</eisbn><abstract>Accurate flood water level prediction are essential for reliable flood forecasting modelling. Although back propagation neural network (BPN) offer advantages for flood water level prediction, nonlinearity due to input parameters are the major issue to this modelling. A novel Extended Kalman Filter (EKF) optimization algorithm was employed in this study to overcome the nonlinearity problem and come out with an optimal ANN for the prediction of flood water level 3 hours in advance. The inputs used in the algorithm were current values of rainfall at the flood location and three upstream locations of river water levels. The BPN model was trained and tested successfully with Root Mean Square Error (RMSE) and loss function (V) close to zero.</abstract><pub>IEEE</pub><doi>10.1109/ICCSCE.2012.6487204</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9781467331425 |
| ispartof | 2012 IEEE International Conference on Control System, Computing and Engineering, 2012, p.535-538 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6487204 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Back Propagation Neural Network (BPN) Extended Kalman Filter (EKF) Flood Modelling and Prediction |
| title | Artificial neural network modelling and flood water level prediction using extended Kalman filter |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T03%3A43%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Artificial%20neural%20network%20modelling%20and%20flood%20water%20level%20prediction%20using%20extended%20Kalman%20filter&rft.btitle=2012%20IEEE%20International%20Conference%20on%20Control%20System,%20Computing%20and%20Engineering&rft.au=Adnan,%20R.&rft.date=2012-11&rft.spage=535&rft.epage=538&rft.pages=535-538&rft.isbn=9781467331425&rft.isbn_list=1467331422&rft_id=info:doi/10.1109/ICCSCE.2012.6487204&rft_dat=%3Cieee_6IE%3E6487204%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=1467331414&rft.eisbn_list=1467331430&rft.eisbn_list=9781467331432&rft.eisbn_list=9781467331418&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6487204&rfr_iscdi=true |