Comparative Study of Conventional and Computerized Reconstruction Techniques for Flow Time Series Data of Hydrometric Station
One of the undeniable requirements in hydrological forecasting and water resources studies is the availability of reliable information. Due to the various reasons, time series data are not usually complete in those surveys, therefore; reconstruction techniques are highly required to complete the mis...
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| Veröffentlicht in: | Water resources management 2019-04, Vol.33 (6), p.1913-1926 |
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| creator | Nozari, Hamed Tavakoli, Fateme Mohamadi, Mohamad |
| description | One of the undeniable requirements in hydrological forecasting and water resources studies is the availability of reliable information. Due to the various reasons, time series data are not usually complete in those surveys, therefore; reconstruction techniques are highly required to complete the missing data. This research was undertaken to evaluate the efficiency of the computer-based methods namely artificial neural network, support vector machine, ARIMA, and ARMAX along with conventional reconstruction strategies of ratio analysis, Fragment, and Thomas-Fiering. As a case study, the monthly flow data of seven hydrometric stations in the Urmia Lake Basin were employed during a 15-year period. The results were then compared using the evaluation criteria of the correlation coefficient (R
2
), root mean square error (RMSE), standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and standard error (SE). Based on key results, computerized methods had higher accuracy than conventional ones in data reconstruction. In terms of efficiency, among the computer-based methods, the support vector machine, ARMAX, artificial neural network, and ARIMA model were ranked from the first to fourth in missing data regeneration. |
| doi_str_mv | 10.1007/s11269-019-2203-x |
| format | Article |
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2
), root mean square error (RMSE), standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and standard error (SE). Based on key results, computerized methods had higher accuracy than conventional ones in data reconstruction. In terms of efficiency, among the computer-based methods, the support vector machine, ARMAX, artificial neural network, and ARIMA model were ranked from the first to fourth in missing data regeneration.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-019-2203-x</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Artificial neural networks ; Atmospheric Sciences ; Autoregressive models ; Case studies ; Civil Engineering ; Comparative analysis ; Comparative studies ; Computing time ; Correlation coefficient ; Correlation coefficients ; Data ; Earth and Environmental Science ; Earth Sciences ; Efficiency ; Environment ; Evaluation ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Hydrology ; Hydrology/Water Resources ; Hydrometric stations ; Lake basins ; Lakes ; Methods ; Missing data ; Neural networks ; Reconstruction ; Regeneration ; Regeneration (biological) ; Root-mean-square errors ; Standard error ; Support vector machines ; Surveys ; Time series ; Water resources</subject><ispartof>Water resources management, 2019-04, Vol.33 (6), p.1913-1926</ispartof><rights>Springer Nature B.V. 2019</rights><rights>Water Resources Management is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-99dcf8a2b11d0ae5c8ec3dbeea20a5fd62100d66c2808ce8263685ab5562de123</citedby><cites>FETCH-LOGICAL-c316t-99dcf8a2b11d0ae5c8ec3dbeea20a5fd62100d66c2808ce8263685ab5562de123</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/s11269-019-2203-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-019-2203-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Nozari, Hamed</creatorcontrib><creatorcontrib>Tavakoli, Fateme</creatorcontrib><creatorcontrib>Mohamadi, Mohamad</creatorcontrib><title>Comparative Study of Conventional and Computerized Reconstruction Techniques for Flow Time Series Data of Hydrometric Station</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>One of the undeniable requirements in hydrological forecasting and water resources studies is the availability of reliable information. Due to the various reasons, time series data are not usually complete in those surveys, therefore; reconstruction techniques are highly required to complete the missing data. This research was undertaken to evaluate the efficiency of the computer-based methods namely artificial neural network, support vector machine, ARIMA, and ARMAX along with conventional reconstruction strategies of ratio analysis, Fragment, and Thomas-Fiering. As a case study, the monthly flow data of seven hydrometric stations in the Urmia Lake Basin were employed during a 15-year period. The results were then compared using the evaluation criteria of the correlation coefficient (R
2
), root mean square error (RMSE), standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and standard error (SE). Based on key results, computerized methods had higher accuracy than conventional ones in data reconstruction. In terms of efficiency, among the computer-based methods, the support vector machine, ARMAX, artificial neural network, and ARIMA model were ranked from the first to fourth in missing data regeneration.</description><subject>Artificial neural networks</subject><subject>Atmospheric Sciences</subject><subject>Autoregressive models</subject><subject>Case studies</subject><subject>Civil Engineering</subject><subject>Comparative analysis</subject><subject>Comparative studies</subject><subject>Computing time</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Data</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Efficiency</subject><subject>Environment</subject><subject>Evaluation</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Hydrology</subject><subject>Hydrology/Water Resources</subject><subject>Hydrometric stations</subject><subject>Lake basins</subject><subject>Lakes</subject><subject>Methods</subject><subject>Missing data</subject><subject>Neural networks</subject><subject>Reconstruction</subject><subject>Regeneration</subject><subject>Regeneration (biological)</subject><subject>Root-mean-square errors</subject><subject>Standard error</subject><subject>Support vector machines</subject><subject>Surveys</subject><subject>Time series</subject><subject>Water 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and water resources studies is the availability of reliable information. Due to the various reasons, time series data are not usually complete in those surveys, therefore; reconstruction techniques are highly required to complete the missing data. This research was undertaken to evaluate the efficiency of the computer-based methods namely artificial neural network, support vector machine, ARIMA, and ARMAX along with conventional reconstruction strategies of ratio analysis, Fragment, and Thomas-Fiering. As a case study, the monthly flow data of seven hydrometric stations in the Urmia Lake Basin were employed during a 15-year period. The results were then compared using the evaluation criteria of the correlation coefficient (R
2
), root mean square error (RMSE), standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and standard error (SE). Based on key results, computerized methods had higher accuracy than conventional ones in data reconstruction. In terms of efficiency, among the computer-based methods, the support vector machine, ARMAX, artificial neural network, and ARIMA model were ranked from the first to fourth in missing data regeneration.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-019-2203-x</doi><tpages>14</tpages></addata></record> |
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| subjects | Artificial neural networks Atmospheric Sciences Autoregressive models Case studies Civil Engineering Comparative analysis Comparative studies Computing time Correlation coefficient Correlation coefficients Data Earth and Environmental Science Earth Sciences Efficiency Environment Evaluation Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrology Hydrology/Water Resources Hydrometric stations Lake basins Lakes Methods Missing data Neural networks Reconstruction Regeneration Regeneration (biological) Root-mean-square errors Standard error Support vector machines Surveys Time series Water resources |
| title | Comparative Study of Conventional and Computerized Reconstruction Techniques for Flow Time Series Data of Hydrometric Station |
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