Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level
Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly bas...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lv, Jie [verfasserIn] Peng, Junhuan [verfasserIn] Zhang, Dezhao [verfasserIn] Zhao, Hui [verfasserIn] Li, Bin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the Indian Society of Remote Sensing - Neu Delhi : Springer India, 2008, 46(2018), 10 vom: 20. Juli, Seite 1595-1603 |
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| Übergeordnetes Werk: |
volume:46 ; year:2018 ; number:10 ; day:20 ; month:07 ; pages:1595-1603 |
| Links: |
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| DOI / URN: |
10.1007/s12524-018-0812-0 |
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| Katalog-ID: |
SPR026023318 |
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| 520 | |a Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. | ||
| 650 | 4 | |a Poyang Lake |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Water area |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Water level |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Robust estimation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Equivalent weight |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Peng, Junhuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Dezhao |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Hui |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Bin |e verfasserin |4 aut | |
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10.1007/s12524-018-0812-0 doi (DE-627)SPR026023318 (SPR)s12524-018-0812-0-e DE-627 ger DE-627 rakwb eng 550 ASE Lv, Jie verfasserin aut Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. Poyang Lake (dpeaa)DE-He213 Water area (dpeaa)DE-He213 Water level (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Equivalent weight (dpeaa)DE-He213 Peng, Junhuan verfasserin aut Zhang, Dezhao verfasserin aut Zhao, Hui verfasserin aut Li, Bin verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2018), 10 vom: 20. Juli, Seite 1595-1603 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2018 number:10 day:20 month:07 pages:1595-1603 https://dx.doi.org/10.1007/s12524-018-0812-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2018 10 20 07 1595-1603 |
| spelling |
10.1007/s12524-018-0812-0 doi (DE-627)SPR026023318 (SPR)s12524-018-0812-0-e DE-627 ger DE-627 rakwb eng 550 ASE Lv, Jie verfasserin aut Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. Poyang Lake (dpeaa)DE-He213 Water area (dpeaa)DE-He213 Water level (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Equivalent weight (dpeaa)DE-He213 Peng, Junhuan verfasserin aut Zhang, Dezhao verfasserin aut Zhao, Hui verfasserin aut Li, Bin verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2018), 10 vom: 20. Juli, Seite 1595-1603 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2018 number:10 day:20 month:07 pages:1595-1603 https://dx.doi.org/10.1007/s12524-018-0812-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2018 10 20 07 1595-1603 |
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10.1007/s12524-018-0812-0 doi (DE-627)SPR026023318 (SPR)s12524-018-0812-0-e DE-627 ger DE-627 rakwb eng 550 ASE Lv, Jie verfasserin aut Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. Poyang Lake (dpeaa)DE-He213 Water area (dpeaa)DE-He213 Water level (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Equivalent weight (dpeaa)DE-He213 Peng, Junhuan verfasserin aut Zhang, Dezhao verfasserin aut Zhao, Hui verfasserin aut Li, Bin verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2018), 10 vom: 20. Juli, Seite 1595-1603 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2018 number:10 day:20 month:07 pages:1595-1603 https://dx.doi.org/10.1007/s12524-018-0812-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2018 10 20 07 1595-1603 |
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10.1007/s12524-018-0812-0 doi (DE-627)SPR026023318 (SPR)s12524-018-0812-0-e DE-627 ger DE-627 rakwb eng 550 ASE Lv, Jie verfasserin aut Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. Poyang Lake (dpeaa)DE-He213 Water area (dpeaa)DE-He213 Water level (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Equivalent weight (dpeaa)DE-He213 Peng, Junhuan verfasserin aut Zhang, Dezhao verfasserin aut Zhao, Hui verfasserin aut Li, Bin verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2018), 10 vom: 20. Juli, Seite 1595-1603 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2018 number:10 day:20 month:07 pages:1595-1603 https://dx.doi.org/10.1007/s12524-018-0812-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2018 10 20 07 1595-1603 |
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10.1007/s12524-018-0812-0 doi (DE-627)SPR026023318 (SPR)s12524-018-0812-0-e DE-627 ger DE-627 rakwb eng 550 ASE Lv, Jie verfasserin aut Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. Poyang Lake (dpeaa)DE-He213 Water area (dpeaa)DE-He213 Water level (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Equivalent weight (dpeaa)DE-He213 Peng, Junhuan verfasserin aut Zhang, Dezhao verfasserin aut Zhao, Hui verfasserin aut Li, Bin verfasserin aut Enthalten in Journal of the Indian Society of Remote Sensing Neu Delhi : Springer India, 2008 46(2018), 10 vom: 20. Juli, Seite 1595-1603 (DE-627)573088853 (DE-600)2439566-3 0974-3006 nnns volume:46 year:2018 number:10 day:20 month:07 pages:1595-1603 https://dx.doi.org/10.1007/s12524-018-0812-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2018 10 20 07 1595-1603 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR026023318</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111132734.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12524-018-0812-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR026023318</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12524-018-0812-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lv, Jie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. 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Juli, Seite 1595-1603</subfield><subfield code="w">(DE-627)573088853</subfield><subfield code="w">(DE-600)2439566-3</subfield><subfield code="x">0974-3006</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:46</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:10</subfield><subfield code="g">day:20</subfield><subfield code="g">month:07</subfield><subfield code="g">pages:1595-1603</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s12524-018-0812-0</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield 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Lv, Jie |
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550 ASE Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level Poyang Lake (dpeaa)DE-He213 Water area (dpeaa)DE-He213 Water level (dpeaa)DE-He213 Robust estimation (dpeaa)DE-He213 Equivalent weight (dpeaa)DE-He213 |
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Lv, Jie Peng, Junhuan Zhang, Dezhao Zhao, Hui Li, Bin |
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application of robust estimation method in study of relationship between lake’s water area and water level |
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Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level |
| abstract |
Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. |
| abstractGer |
Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. |
| abstract_unstemmed |
Abstract Better understanding of the relationship between lake’s water area and water level plays an important role for the monitoring of floods and droughts. At present, there are many analysis and research on the relationship between water area and level, but the estimation criteria are mostly based on ordinary least square, which has no ability to resist the gross error. In this paper, the equivalent weight estimation method with robustness ability is introduced. We use the proposed method in study of the Poyang Lake which is the largest freshwater lake in China. The area extraction data and water level records spans the period from 2009 to 2013. Four models (linear, exponential, logarithmic and quadratic polynomial) of water area and water level are constructed by regression analysis. Meanwhile, two kinds of common weight function factors are applied to analyze the quadratic curve model. The experimental results show that the quadratic polynomial fitting performs best, and the solution of the equivalent weight function method is closer to the realism than least square. We note that the proposed robust estimation method can dynamically monitor the water area and water level, which provides a theoretical basis for similar research. |
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| title_short |
Application of Robust Estimation Method in Study of Relationship Between Lake’s Water Area and Water Level |
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https://dx.doi.org/10.1007/s12524-018-0812-0 |
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| author2 |
Peng, Junhuan Zhang, Dezhao Zhao, Hui Li, Bin |
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| doi_str |
10.1007/s12524-018-0812-0 |
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2024-07-03T18:24:52.456Z |
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| score |
7.401532 |