Missing river discharge data imputation approach using artificial neural network
The issue with missing data in hydrological models are very common and it occurs when no data value was stored during observation. In modelling, the missing data can affect the conclusion that can be drawn from the dataset. This paper presents the study on Levenberg-Marquadt back propagation algorit...
Gespeichert in:
| Veröffentlicht in: | ARPN journal of engineering and applied sciences 2015-12, Vol.10 (22) |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 22 |
| container_start_page | |
| container_title | ARPN journal of engineering and applied sciences |
| container_volume | 10 |
| creator | Mispan, M R Rahman, N F A Ali, M F Khalid, K Bakar, M H A Haron, S H |
| description | The issue with missing data in hydrological models are very common and it occurs when no data value was stored during observation. In modelling, the missing data can affect the conclusion that can be drawn from the dataset. This paper presents the study on Levenberg-Marquadt back propagation algorithm in predicting missing stream flow data in Langat River Basin. Data series from the upper part of Langat River Basin were applied to build the Artificial Neural Network model. The result indicated good performance of the model with Pearson Correlation, r = 0.97261 for training data and overall data shows r = 0.91925. The study reveals that Levenberg-Marquadt back propagation algorithm for ANN can simulate well in the daily missing stream flow prediction if the model customizes with good configuration. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_miscellaneous_1808047497</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1808047497</sourcerecordid><originalsourceid>FETCH-LOGICAL-p103t-d96d9daa6f3afaf318f22ed0d9b68d393bc5002797fd86bb3cbb173794c929db3</originalsourceid><addsrcrecordid>eNpNjrtOAzEURC0EElHIP7ikWcleG9u3RBEvKSgUUEfXr8Sw2V1sL_w-q0DBNGeK0eickQU3HBqlmDn_1y_JqpR3NkeC1EYsyMtzKiX1e5rTV8jUp-IOmPeBeqxI03GcKtY09BTHMQ_oDnQ6zTHXFJNL2NE-TPmE-j3kjytyEbErYfXHJXm7v3tdPzab7cPT-nbTjJyJ2nhQHjyiigIjRsFNbNvgmQerjBcgrLthrNWgozfKWuGs5VpokA5a8FYsyfXv76z1OYVSd8fZPXQd9mGYyo4bZpjUErT4AYVCUJg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1808047497</pqid></control><display><type>article</type><title>Missing river discharge data imputation approach using artificial neural network</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Mispan, M R ; Rahman, N F A ; Ali, M F ; Khalid, K ; Bakar, M H A ; Haron, S H</creator><creatorcontrib>Mispan, M R ; Rahman, N F A ; Ali, M F ; Khalid, K ; Bakar, M H A ; Haron, S H</creatorcontrib><description>The issue with missing data in hydrological models are very common and it occurs when no data value was stored during observation. In modelling, the missing data can affect the conclusion that can be drawn from the dataset. This paper presents the study on Levenberg-Marquadt back propagation algorithm in predicting missing stream flow data in Langat River Basin. Data series from the upper part of Langat River Basin were applied to build the Artificial Neural Network model. The result indicated good performance of the model with Pearson Correlation, r = 0.97261 for training data and overall data shows r = 0.91925. The study reveals that Levenberg-Marquadt back propagation algorithm for ANN can simulate well in the daily missing stream flow prediction if the model customizes with good configuration.</description><identifier>ISSN: 1819-6608</identifier><identifier>EISSN: 1819-6608</identifier><language>eng</language><subject>Artificial neural networks ; Back propagation algorithms ; Learning theory ; Mathematical models ; Missing data ; Neural networks ; River basins ; Water runoff</subject><ispartof>ARPN journal of engineering and applied sciences, 2015-12, Vol.10 (22)</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784</link.rule.ids></links><search><creatorcontrib>Mispan, M R</creatorcontrib><creatorcontrib>Rahman, N F A</creatorcontrib><creatorcontrib>Ali, M F</creatorcontrib><creatorcontrib>Khalid, K</creatorcontrib><creatorcontrib>Bakar, M H A</creatorcontrib><creatorcontrib>Haron, S H</creatorcontrib><title>Missing river discharge data imputation approach using artificial neural network</title><title>ARPN journal of engineering and applied sciences</title><description>The issue with missing data in hydrological models are very common and it occurs when no data value was stored during observation. In modelling, the missing data can affect the conclusion that can be drawn from the dataset. This paper presents the study on Levenberg-Marquadt back propagation algorithm in predicting missing stream flow data in Langat River Basin. Data series from the upper part of Langat River Basin were applied to build the Artificial Neural Network model. The result indicated good performance of the model with Pearson Correlation, r = 0.97261 for training data and overall data shows r = 0.91925. The study reveals that Levenberg-Marquadt back propagation algorithm for ANN can simulate well in the daily missing stream flow prediction if the model customizes with good configuration.</description><subject>Artificial neural networks</subject><subject>Back propagation algorithms</subject><subject>Learning theory</subject><subject>Mathematical models</subject><subject>Missing data</subject><subject>Neural networks</subject><subject>River basins</subject><subject>Water runoff</subject><issn>1819-6608</issn><issn>1819-6608</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNpNjrtOAzEURC0EElHIP7ikWcleG9u3RBEvKSgUUEfXr8Sw2V1sL_w-q0DBNGeK0eickQU3HBqlmDn_1y_JqpR3NkeC1EYsyMtzKiX1e5rTV8jUp-IOmPeBeqxI03GcKtY09BTHMQ_oDnQ6zTHXFJNL2NE-TPmE-j3kjytyEbErYfXHJXm7v3tdPzab7cPT-nbTjJyJ2nhQHjyiigIjRsFNbNvgmQerjBcgrLthrNWgozfKWuGs5VpokA5a8FYsyfXv76z1OYVSd8fZPXQd9mGYyo4bZpjUErT4AYVCUJg</recordid><startdate>20151201</startdate><enddate>20151201</enddate><creator>Mispan, M R</creator><creator>Rahman, N F A</creator><creator>Ali, M F</creator><creator>Khalid, K</creator><creator>Bakar, M H A</creator><creator>Haron, S H</creator><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20151201</creationdate><title>Missing river discharge data imputation approach using artificial neural network</title><author>Mispan, M R ; Rahman, N F A ; Ali, M F ; Khalid, K ; Bakar, M H A ; Haron, S H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p103t-d96d9daa6f3afaf318f22ed0d9b68d393bc5002797fd86bb3cbb173794c929db3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Artificial neural networks</topic><topic>Back propagation algorithms</topic><topic>Learning theory</topic><topic>Mathematical models</topic><topic>Missing data</topic><topic>Neural networks</topic><topic>River basins</topic><topic>Water runoff</topic><toplevel>online_resources</toplevel><creatorcontrib>Mispan, M R</creatorcontrib><creatorcontrib>Rahman, N F A</creatorcontrib><creatorcontrib>Ali, M F</creatorcontrib><creatorcontrib>Khalid, K</creatorcontrib><creatorcontrib>Bakar, M H A</creatorcontrib><creatorcontrib>Haron, S H</creatorcontrib><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>ARPN journal of engineering and applied sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mispan, M R</au><au>Rahman, N F A</au><au>Ali, M F</au><au>Khalid, K</au><au>Bakar, M H A</au><au>Haron, S H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Missing river discharge data imputation approach using artificial neural network</atitle><jtitle>ARPN journal of engineering and applied sciences</jtitle><date>2015-12-01</date><risdate>2015</risdate><volume>10</volume><issue>22</issue><issn>1819-6608</issn><eissn>1819-6608</eissn><abstract>The issue with missing data in hydrological models are very common and it occurs when no data value was stored during observation. In modelling, the missing data can affect the conclusion that can be drawn from the dataset. This paper presents the study on Levenberg-Marquadt back propagation algorithm in predicting missing stream flow data in Langat River Basin. Data series from the upper part of Langat River Basin were applied to build the Artificial Neural Network model. The result indicated good performance of the model with Pearson Correlation, r = 0.97261 for training data and overall data shows r = 0.91925. The study reveals that Levenberg-Marquadt back propagation algorithm for ANN can simulate well in the daily missing stream flow prediction if the model customizes with good configuration.</abstract></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1819-6608 |
| ispartof | ARPN journal of engineering and applied sciences, 2015-12, Vol.10 (22) |
| issn | 1819-6608 1819-6608 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1808047497 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Artificial neural networks Back propagation algorithms Learning theory Mathematical models Missing data Neural networks River basins Water runoff |
| title | Missing river discharge data imputation approach using artificial neural network |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T17%3A51%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Missing%20river%20discharge%20data%20imputation%20approach%20using%20artificial%20neural%20network&rft.jtitle=ARPN%20journal%20of%20engineering%20and%20applied%20sciences&rft.au=Mispan,%20M%20R&rft.date=2015-12-01&rft.volume=10&rft.issue=22&rft.issn=1819-6608&rft.eissn=1819-6608&rft_id=info:doi/&rft_dat=%3Cproquest%3E1808047497%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1808047497&rft_id=info:pmid/&rfr_iscdi=true |