Particle swarm optimization trained neural network for aquifer parameter estimation
Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ch, Sudheer [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Anmerkung: |
© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 |
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| Übergeordnetes Werk: |
Enthalten in: KSCE journal of civil engineering - Seoul : Korean Soc. of Civil Engineers, 1997, 16(2012), 3 vom: 29. Feb., Seite 298-307 |
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| Übergeordnetes Werk: |
volume:16 ; year:2012 ; number:3 ; day:29 ; month:02 ; pages:298-307 |
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| DOI / URN: |
10.1007/s12205-012-1452-5 |
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| Katalog-ID: |
SPR025257870 |
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| 520 | |a Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. | ||
| 650 | 4 | |a neural networks technique |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a particle swarm optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a aquifer parameters estimation |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a sensitivity analysis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Mathur, Shashi |4 aut | |
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10.1007/s12205-012-1452-5 doi (DE-627)SPR025257870 (SPR)s12205-012-1452-5-e DE-627 ger DE-627 rakwb eng Ch, Sudheer verfasserin aut Particle swarm optimization trained neural network for aquifer parameter estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. neural networks technique (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 aquifer parameters estimation (dpeaa)DE-He213 sensitivity analysis (dpeaa)DE-He213 Mathur, Shashi aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 3 vom: 29. Feb., Seite 298-307 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:3 day:29 month:02 pages:298-307 https://dx.doi.org/10.1007/s12205-012-1452-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2472 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 16 2012 3 29 02 298-307 |
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10.1007/s12205-012-1452-5 doi (DE-627)SPR025257870 (SPR)s12205-012-1452-5-e DE-627 ger DE-627 rakwb eng Ch, Sudheer verfasserin aut Particle swarm optimization trained neural network for aquifer parameter estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. neural networks technique (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 aquifer parameters estimation (dpeaa)DE-He213 sensitivity analysis (dpeaa)DE-He213 Mathur, Shashi aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 3 vom: 29. Feb., Seite 298-307 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:3 day:29 month:02 pages:298-307 https://dx.doi.org/10.1007/s12205-012-1452-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2472 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 16 2012 3 29 02 298-307 |
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10.1007/s12205-012-1452-5 doi (DE-627)SPR025257870 (SPR)s12205-012-1452-5-e DE-627 ger DE-627 rakwb eng Ch, Sudheer verfasserin aut Particle swarm optimization trained neural network for aquifer parameter estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. neural networks technique (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 aquifer parameters estimation (dpeaa)DE-He213 sensitivity analysis (dpeaa)DE-He213 Mathur, Shashi aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 3 vom: 29. Feb., Seite 298-307 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:3 day:29 month:02 pages:298-307 https://dx.doi.org/10.1007/s12205-012-1452-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2472 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 16 2012 3 29 02 298-307 |
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10.1007/s12205-012-1452-5 doi (DE-627)SPR025257870 (SPR)s12205-012-1452-5-e DE-627 ger DE-627 rakwb eng Ch, Sudheer verfasserin aut Particle swarm optimization trained neural network for aquifer parameter estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. neural networks technique (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 aquifer parameters estimation (dpeaa)DE-He213 sensitivity analysis (dpeaa)DE-He213 Mathur, Shashi aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 3 vom: 29. Feb., Seite 298-307 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:3 day:29 month:02 pages:298-307 https://dx.doi.org/10.1007/s12205-012-1452-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2472 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 16 2012 3 29 02 298-307 |
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10.1007/s12205-012-1452-5 doi (DE-627)SPR025257870 (SPR)s12205-012-1452-5-e DE-627 ger DE-627 rakwb eng Ch, Sudheer verfasserin aut Particle swarm optimization trained neural network for aquifer parameter estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. neural networks technique (dpeaa)DE-He213 particle swarm optimization (dpeaa)DE-He213 aquifer parameters estimation (dpeaa)DE-He213 sensitivity analysis (dpeaa)DE-He213 Mathur, Shashi aut Enthalten in KSCE journal of civil engineering Seoul : Korean Soc. of Civil Engineers, 1997 16(2012), 3 vom: 29. Feb., Seite 298-307 (DE-627)57517238X (DE-600)2446036-9 1976-3808 nnns volume:16 year:2012 number:3 day:29 month:02 pages:298-307 https://dx.doi.org/10.1007/s12205-012-1452-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_2472 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 16 2012 3 29 02 298-307 |
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Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. 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Ch, Sudheer misc neural networks technique misc particle swarm optimization misc aquifer parameters estimation misc sensitivity analysis Particle swarm optimization trained neural network for aquifer parameter estimation |
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particle swarm optimization trained neural network for aquifer parameter estimation |
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Particle swarm optimization trained neural network for aquifer parameter estimation |
| abstract |
Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 |
| abstractGer |
Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 |
| abstract_unstemmed |
Abstract Numerical simulation in aquifers require knowledge of parameters that govern flow through aquifers, however, at times, these parameters are not available. Estimation of such parameters has thus gained importance in the recent years and researchers have suggested various ways by which these parameters can be obtained. Amongst the various methods used for parameter estimation, Artificial Neural Networks (ANN) has shown promise in determining parameters for non leaky confined aquifers. Usually some gradient algorithms including the Back Propagation (BP) technique are used for training a network, however these procedures exhibit slow convergence. Besides this, the solution gets easily entrapped in a local minima. The ANN proposed in this study employs a Particle Swarm Optimization (PSO) technique to train the perceptrons to predict the storage coefficient and transmissivity of aquifers. PSO technique could be an effective alternate training algorithm for ANN’s since it is found to much accurate when compared to the existing conventional algorithms. Besides this, since PSO is a heuristic optimization technique, a global optimal solution can be obtained. Further, a sensitivity analysis is later carried out in the study to evaluate the most suitable ANN characteristics which includes the learning rate, the momentum factor, and the number of neurons in the input, hidden and output layers. Also, the impact of maximum velocity and acceleration constants of PSO on ANN convergence is studied so as to obtain the best possible value of parameters to minimize error. Further the proposed Particle Swarm Optimization trained Neural Network is employed in aquifer Parameter Estimation for the specific cases and the results are compared with the other existing gradient algorithms. © Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2012 |
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Particle swarm optimization trained neural network for aquifer parameter estimation |
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https://dx.doi.org/10.1007/s12205-012-1452-5 |
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Mathur, Shashi |
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10.1007/s12205-012-1452-5 |
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2024-07-03T14:51:57.525Z |
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|
| score |
7.399288 |