Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China
Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Xie, Zhiqiang [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media Dordrecht 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Natural hazards - Springer Netherlands, 1988, 77(2015), 2 vom: 28. Feb., Seite 1081-1102 |
|---|---|
| Übergeordnetes Werk: |
volume:77 ; year:2015 ; number:2 ; day:28 ; month:02 ; pages:1081-1102 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11069-015-1648-3 |
|---|
| Katalog-ID: |
OLC2053669500 |
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| 520 | |a Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. | ||
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| 700 | 1 | |a Jamiesone, Sam |4 aut | |
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10.1007/s11069-015-1648-3 doi (DE-627)OLC2053669500 (DE-He213)s11069-015-1648-3-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Xie, Zhiqiang verfasserin aut Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2015 Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. Artificial neural network Urban drainage system Urban waterlogging simulation Knowledge coupled MATLAB River stage forecast Du, Qingyun aut Ren, Fu aut Zhang, Xiaowei aut Jamiesone, Sam aut Enthalten in Natural hazards Springer Netherlands, 1988 77(2015), 2 vom: 28. Feb., Seite 1081-1102 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:77 year:2015 number:2 day:28 month:02 pages:1081-1102 https://doi.org/10.1007/s11069-015-1648-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 77 2015 2 28 02 1081-1102 |
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10.1007/s11069-015-1648-3 doi (DE-627)OLC2053669500 (DE-He213)s11069-015-1648-3-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Xie, Zhiqiang verfasserin aut Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2015 Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. Artificial neural network Urban drainage system Urban waterlogging simulation Knowledge coupled MATLAB River stage forecast Du, Qingyun aut Ren, Fu aut Zhang, Xiaowei aut Jamiesone, Sam aut Enthalten in Natural hazards Springer Netherlands, 1988 77(2015), 2 vom: 28. Feb., Seite 1081-1102 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:77 year:2015 number:2 day:28 month:02 pages:1081-1102 https://doi.org/10.1007/s11069-015-1648-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 77 2015 2 28 02 1081-1102 |
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10.1007/s11069-015-1648-3 doi (DE-627)OLC2053669500 (DE-He213)s11069-015-1648-3-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Xie, Zhiqiang verfasserin aut Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2015 Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. Artificial neural network Urban drainage system Urban waterlogging simulation Knowledge coupled MATLAB River stage forecast Du, Qingyun aut Ren, Fu aut Zhang, Xiaowei aut Jamiesone, Sam aut Enthalten in Natural hazards Springer Netherlands, 1988 77(2015), 2 vom: 28. Feb., Seite 1081-1102 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:77 year:2015 number:2 day:28 month:02 pages:1081-1102 https://doi.org/10.1007/s11069-015-1648-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 77 2015 2 28 02 1081-1102 |
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10.1007/s11069-015-1648-3 doi (DE-627)OLC2053669500 (DE-He213)s11069-015-1648-3-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Xie, Zhiqiang verfasserin aut Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2015 Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. Artificial neural network Urban drainage system Urban waterlogging simulation Knowledge coupled MATLAB River stage forecast Du, Qingyun aut Ren, Fu aut Zhang, Xiaowei aut Jamiesone, Sam aut Enthalten in Natural hazards Springer Netherlands, 1988 77(2015), 2 vom: 28. Feb., Seite 1081-1102 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:77 year:2015 number:2 day:28 month:02 pages:1081-1102 https://doi.org/10.1007/s11069-015-1648-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 77 2015 2 28 02 1081-1102 |
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10.1007/s11069-015-1648-3 doi (DE-627)OLC2053669500 (DE-He213)s11069-015-1648-3-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn Xie, Zhiqiang verfasserin aut Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media Dordrecht 2015 Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. Artificial neural network Urban drainage system Urban waterlogging simulation Knowledge coupled MATLAB River stage forecast Du, Qingyun aut Ren, Fu aut Zhang, Xiaowei aut Jamiesone, Sam aut Enthalten in Natural hazards Springer Netherlands, 1988 77(2015), 2 vom: 28. Feb., Seite 1081-1102 (DE-627)131010271 (DE-600)1088547-X (DE-576)03285272X 0921-030X nnns volume:77 year:2015 number:2 day:28 month:02 pages:1081-1102 https://doi.org/10.1007/s11069-015-1648-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-MAT SSG-OPC-GGO SSG-OPC-MAT GBV_ILN_70 AR 77 2015 2 28 02 1081-1102 |
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Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China |
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Xie, Zhiqiang |
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Natural hazards |
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Natural hazards |
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eng |
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2015 |
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Xie, Zhiqiang Du, Qingyun Ren, Fu Zhang, Xiaowei Jamiesone, Sam |
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Xie, Zhiqiang |
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10.1007/s11069-015-1648-3 |
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550 |
| title_sort |
improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with bp artificial neural networks: a case study of panlong river, kunming, china |
| title_auth |
Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China |
| abstract |
Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. © Springer Science+Business Media Dordrecht 2015 |
| abstractGer |
Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. © Springer Science+Business Media Dordrecht 2015 |
| abstract_unstemmed |
Abstract Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations. © Springer Science+Business Media Dordrecht 2015 |
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| title_short |
Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China |
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https://doi.org/10.1007/s11069-015-1648-3 |
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Du, Qingyun Ren, Fu Zhang, Xiaowei Jamiesone, Sam |
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