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Modelling of Reservoir Water Release Decision Using Neural Network and Temporal Pattern of Reservoir Water Level
The reservoir is one of flood mitigation methods that aim to reduce the effect of flood at downstream flood prone areas. At the same time the reservoir also serves other purposes. Through modelling, how the reservoir operator made decisions in the past can be revealed. Consequently, the information...
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| creator | Mokhtar, Suriyati Abdul Ishak, Wan Hussain Wan Md Norwawi, Norita |
| description | The reservoir is one of flood mitigation methods that aim to reduce the effect of flood at downstream flood prone areas. At the same time the reservoir also serves other purposes. Through modelling, how the reservoir operator made decisions in the past can be revealed. Consequently, the information can be used to guide reservoir operator making present decision especially during emergency situations such as flood and drought. This paper discussed modelling of reservoir water release decision using Neural Network (NN) and the temporal pattern of reservoir water level. Temporal pattern is used to represent the time delay as the rainfall upstream may not directly raise the reservoir water level. The flow of water may take some time to reach the reservoir due to the location. Seven NN models have been developed and tested. The findings show that the NN model with 5-25-1 architecture demonstrate the best performance compare to the other models. |
| doi_str_mv | 10.1109/ISMS.2014.27 |
| format | conference_proceeding |
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At the same time the reservoir also serves other purposes. Through modelling, how the reservoir operator made decisions in the past can be revealed. Consequently, the information can be used to guide reservoir operator making present decision especially during emergency situations such as flood and drought. This paper discussed modelling of reservoir water release decision using Neural Network (NN) and the temporal pattern of reservoir water level. Temporal pattern is used to represent the time delay as the rainfall upstream may not directly raise the reservoir water level. The flow of water may take some time to reach the reservoir due to the location. Seven NN models have been developed and tested. 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At the same time the reservoir also serves other purposes. Through modelling, how the reservoir operator made decisions in the past can be revealed. Consequently, the information can be used to guide reservoir operator making present decision especially during emergency situations such as flood and drought. This paper discussed modelling of reservoir water release decision using Neural Network (NN) and the temporal pattern of reservoir water level. Temporal pattern is used to represent the time delay as the rainfall upstream may not directly raise the reservoir water level. The flow of water may take some time to reach the reservoir due to the location. Seven NN models have been developed and tested. The findings show that the NN model with 5-25-1 architecture demonstrate the best performance compare to the other models.</description><subject>Artificial neural networks</subject><subject>Computational modeling</subject><subject>Floods</subject><subject>Neural Network</subject><subject>Reservoir Modelling</subject><subject>Reservoir Water Level</subject><subject>Reservoir Water Release Decision</subject><subject>Reservoirs</subject><subject>Temporal Data Mining</subject><subject>Training</subject><issn>2166-0662</issn><issn>2166-0670</issn><isbn>9781479938575</isbn><isbn>1479938572</isbn><isbn>1479938580</isbn><isbn>9781479938582</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNptjE1LAzEYhKMoKLU3b17yB7bmzebzKPWr0KrYFo8l2bwr0e1uSdaK_94WxZOnZ2aYGULOgY0AmL2czGfzEWcgRlwfkKHVBoS2tjRSy0NyykGpginNjv604idkmPMbYwyYtsaaU7KZdQGbJravtKvpM2ZM2y4m-uJ6TDvfoMtIr7GKOXYtXeZ98wE_kmt26D-79E5dG-gC15tuHz65frds_3ub4habM3Jcuybj8JcDsry9WYzvi-nj3WR8NS0ih7IvQFRaAgajSlA1Z74SstRco8DaBaalBYECQqikl7YCJypvlZe-Ctx7xssBufj5jYi42qS4dulrpblhxvLyG5GzXMM</recordid><startdate>20140101</startdate><enddate>20140101</enddate><creator>Mokhtar, Suriyati Abdul</creator><creator>Ishak, Wan Hussain Wan</creator><creator>Md Norwawi, Norita</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20140101</creationdate><title>Modelling of Reservoir Water Release Decision Using Neural Network and Temporal Pattern of Reservoir Water Level</title><author>Mokhtar, Suriyati Abdul ; Ishak, Wan Hussain Wan ; Md Norwawi, Norita</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i213t-14c751ed86316f20bc453727e4efad075914e41ddc5b59c1a4cb96b5bcd2bb023</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Artificial neural networks</topic><topic>Computational modeling</topic><topic>Floods</topic><topic>Neural Network</topic><topic>Reservoir Modelling</topic><topic>Reservoir Water Level</topic><topic>Reservoir Water Release Decision</topic><topic>Reservoirs</topic><topic>Temporal Data Mining</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Mokhtar, Suriyati Abdul</creatorcontrib><creatorcontrib>Ishak, Wan Hussain Wan</creatorcontrib><creatorcontrib>Md Norwawi, Norita</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>Mokhtar, Suriyati Abdul</au><au>Ishak, Wan Hussain Wan</au><au>Md Norwawi, Norita</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Modelling of Reservoir Water Release Decision Using Neural Network and Temporal Pattern of Reservoir Water Level</atitle><btitle>2014 5th International Conference on Intelligent Systems, Modelling and Simulation</btitle><stitle>ISMS</stitle><date>2014-01-01</date><risdate>2014</risdate><spage>127</spage><epage>130</epage><pages>127-130</pages><issn>2166-0662</issn><eissn>2166-0670</eissn><eisbn>9781479938575</eisbn><eisbn>1479938572</eisbn><eisbn>1479938580</eisbn><eisbn>9781479938582</eisbn><coden>IEEPAD</coden><abstract>The reservoir is one of flood mitigation methods that aim to reduce the effect of flood at downstream flood prone areas. At the same time the reservoir also serves other purposes. Through modelling, how the reservoir operator made decisions in the past can be revealed. Consequently, the information can be used to guide reservoir operator making present decision especially during emergency situations such as flood and drought. This paper discussed modelling of reservoir water release decision using Neural Network (NN) and the temporal pattern of reservoir water level. Temporal pattern is used to represent the time delay as the rainfall upstream may not directly raise the reservoir water level. The flow of water may take some time to reach the reservoir due to the location. Seven NN models have been developed and tested. The findings show that the NN model with 5-25-1 architecture demonstrate the best performance compare to the other models.</abstract><pub>IEEE</pub><doi>10.1109/ISMS.2014.27</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 2166-0662 |
| ispartof | 2014 5th International Conference on Intelligent Systems, Modelling and Simulation, 2014, p.127-130 |
| issn | 2166-0662 2166-0670 |
| language | eng |
| recordid | cdi_ieee_primary_7280892 |
| source | IEEE Xplore All Conference Series |
| subjects | Artificial neural networks Computational modeling Floods Neural Network Reservoir Modelling Reservoir Water Level Reservoir Water Release Decision Reservoirs Temporal Data Mining Training |
| title | Modelling of Reservoir Water Release Decision Using Neural Network and Temporal Pattern of Reservoir Water Level |
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