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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems: Lake Eymir Case - Turkey - Couverture souple
Synopsis
Modeling is a helpful tool that might be used to predict the Dissolved Oxygen (DO) level of a lake. Most ecological systems are complex and unstable. In case black box models might be essential instead of deterministic ones. DO in Eymir Lake was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate,Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: developing models with ANN to predict DO level in Lake Eymir with high fidelity to actual DO data, to compare the success of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. “Matlab R 2007b” software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.
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Présentation de l'éditeur
Modeling is a helpful tool that might be used to predict the Dissolved Oxygen (DO) level of a lake. Most ecological systems are complex and unstable. In case black box models might be essential instead of deterministic ones. DO in Eymir Lake was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate,Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: developing models with ANN to predict DO level in Lake Eymir with high fidelity to actual DO data, to compare the success of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. “Matlab R 2007b” software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.
Biographie de l'auteur
Muhittin ASLAN has received his Master of Science Degree o Environmental Engineering in 2008 from METU. Currently he is working for Ministry of Environment and Urbanisation of Turkey. Also he has doctorate studies regarding social environmental sciences, environmental law and international relations in the scope of environmental politics of EU.
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- ÉditeurLAP LAMBERT Academic Publishing
- Date d'édition2011
- ISBN 10 384653708X
- ISBN 13 9783846537084
- ReliureBroché
- Langueanglais
- Nombre de pages140
- Coordonnées du fabricantnon disponible
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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 384653708X
ISBN 13 : 9783846537084
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Aslan MuhittinMuhittin ASLAN has received his Master of Science Degree o Environmental Engineering in 2008 from METU. Currently he is working for Ministry of Environment and Urbanisation of Turkey. Also he has doctorate studies regar. N° de réf. du vendeur 5497496
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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems : Lake Eymir Case - Turkey
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 384653708X
ISBN 13 : 9783846537084
Neuf
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Modeling is a helpful tool that might be used to predict the Dissolved Oxygen (DO) level of a lake. Most ecological systems are complex and unstable. In case black box models might be essential instead of deterministic ones. DO in Eymir Lake was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate,Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: developing models with ANN to predict DO level in Lake Eymir with high fidelity to actual DO data, to compare the success of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. Matlab R 2007b software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted. N° de réf. du vendeur 9783846537084
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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems
Edité par
LAP LAMBERT Academic Publishing Okt 2011, 2011
ISBN 10 : 384653708X
ISBN 13 : 9783846537084
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Modeling is a helpful tool that might be used to predict the Dissolved Oxygen (DO) level of a lake. Most ecological systems are complex and unstable. In case black box models might be essential instead of deterministic ones. DO in Eymir Lake was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate,Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: developing models with ANN to predict DO level in Lake Eymir with high fidelity to actual DO data, to compare the success of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. Matlab R 2007b software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted. 140 pp. Englisch. N° de réf. du vendeur 9783846537084
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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems
Edité par
LAP LAMBERT Academic Publishing Okt 2011, 2011
ISBN 10 : 384653708X
ISBN 13 : 9783846537084
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Taschenbuch. Etat : Neu. Neuware -Modeling is a helpful tool that might be used to predict the Dissolved Oxygen (DO) level of a lake. Most ecological systems are complex and unstable. In case black box models might be essential instead of deterministic ones. DO in Eymir Lake was modeled by using both Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Phosphate, Orthophospate, pH, Chlorophyll-a, Temperature, Alkalinity, Nitrate,Total Kjeldahl Nitrogen, Wind, Precipitation, Air Temperature were the input parameters of ANN and ANFIS. The aims of these modeling studies were: developing models with ANN to predict DO level in Lake Eymir with high fidelity to actual DO data, to compare the success of ANN and ANFIS on DO modeling, to determine the degree of dependence of different parameters on DO. ¿Matlab R 2007b¿ software was used. The results indicated that ANN has high prediction capacity of DO and ANFIS has low with respect to ANN. Failure of ANFIS was due to low functionality of Matlab ANFIS. For ANN Modeling effect of meteorological data on DO data on surface of the lake was successfully described and summer month super saturation DO concentrations were successfully predicted.Books on Demand GmbH, Überseering 33, 22297 Hamburg 140 pp. Englisch. N° de réf. du vendeur 9783846537084
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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems
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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems
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ISBN 13 : 9783846537084
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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems
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Editorial Academica Espanola, 2011
ISBN 10 : 384653708X
ISBN 13 : 9783846537084
Neuf
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Neural Network and Fuzzy Logic Implementation on Lake Ecosystems: Lake Eymir Case - Turkey
Edité par
LAP LAMBERT Academic Publishing, 2011
ISBN 10 : 384653708X
ISBN 13 : 9783846537084
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