262
C. Karul . S. Soyupak
Works of Turkey and Middie East Technical University, Department of
Environmental Engineering through projects supported by TUBoT AK.
References
Altonbilek D, et al. (1995) Gölba" Mogan and Eymir Lakes Water Resources and
Environmental Management Planning Project, Final Report, Middle East Technical
University, Ankara,
Turkey
Bartsch AF, Gakstatter JH (1978) Management Decisions for Lake Systems on a Survey of
Trophic Status, Limiting Nutrients, and Nutrient Loadings in American-Soviet
Symposium on Use of Mathematical Models to Optimize Water Quality Management,
1975, U.S. EPA Office of Research and Development, Environmental Research
laboratory, GulfBreeze, FL, EPA-600/9-78-024, 372-394
Benndorf J, Recknagel F (1982) Problems of application of the ecological model SALMO
to lakes and reservoirs having various trophic states, Ecol. Modelling, 17, 129-145
Brion GM, Lingireddy S (1997) A neural networks approach to identify sources of
microbial contarnination, CSCEIASCE Env. Eng. Conf. Proceedings, Edmonton,
Alberta,anada, 1321-1332
Canfield DC, Langeland KA, Maceina MJ, Haller WT, Shireman JV, Jones JR (1983)
Trophic state classification oflakes with aquatic macrophytes, Can. Jour. Fish Aquatic
Science, 40, 1713-18
Canfield DC, Shireman JV, Coole DE, Haller WT, Watkins CE, Maceina MJ (1984)
Prediction of chlorophyll a concentrations in Florida Lakes: Importance of aqautic
macrophytes, Can. Jour. Fish Aquatic Science, 41, 409-501
Demuth H, Beale M (1998) Neural Network Toolbox User's Guide, The MathWorks Inc.,
Natick, MA
Dillon PJ, Rigler FH (1974) The phosphorus-chlorophyll relationship in lakes, Limnol.
Oceanogr., 19, 767-773
Fu LM (1994) Neural Networks for Computer Intelligence, McGraw-Hill, Inc
Hagan MT, Menhaj M (1994) Training feedforward networks with the Marquardt
algorithm, IEEE Trans.on Neural Networks, 5, 989-993
Jorgensen SE (1976) A eutrophication model for a lake, Ecol. Model., 2, 147-162
Kaml C, Soyupak S, Germen E (1998a) A new approach to mathematical water quality
modelling in Reservoirs: Neural Networks, Int. Rev. of Hydrobiol., 83, 689-696
Kaml C, Soyupak S, Güven E, Aydooan A, Alp E (1998b) Limnolojik veri tabano
geliotirilmesi ve TCP/IP üzerinden WWW arayüzü ile ioletilmesi: Keban Baraj Gölü
Veri Tabano Ömeoi, DSo <;:evre Semineri, Fethiye,Turkey (in Turkish)
Kaml C (1999a) Development of An Artificial Neural Network Model for the Estimation of
Chlorophyll-a in Lakes, PhD Thesis, METU, Department of Environmental
Engineering, Ankara, Turkey
Kaml C, Soyupak S, Yurteri C (1999b) Neural network models as a management tool in
lakes, Hydrobiologia, 408-409, 139 - 144
Kaml C, Soyupak S, <;:ilesiz AF, Akbay N, Germen E (2000) Case studies on the use of
neural networks in eutrophication modeling, Ecol. Model. ,134 (2-3), 145 - 152
C. Karul . S. Soyupak
Works of Turkey and Middie East Technical University, Department of
Environmental Engineering through projects supported by TUBoT AK.
References
Altonbilek D, et al. (1995) Gölba" Mogan and Eymir Lakes Water Resources and
Environmental Management Planning Project, Final Report, Middle East Technical
University, Ankara,
Turkey
Bartsch AF, Gakstatter JH (1978) Management Decisions for Lake Systems on a Survey of
Trophic Status, Limiting Nutrients, and Nutrient Loadings in American-Soviet
Symposium on Use of Mathematical Models to Optimize Water Quality Management,
1975, U.S. EPA Office of Research and Development, Environmental Research
laboratory, GulfBreeze, FL, EPA-600/9-78-024, 372-394
Benndorf J, Recknagel F (1982) Problems of application of the ecological model SALMO
to lakes and reservoirs having various trophic states, Ecol. Modelling, 17, 129-145
Brion GM, Lingireddy S (1997) A neural networks approach to identify sources of
microbial contarnination, CSCEIASCE Env. Eng. Conf. Proceedings, Edmonton,
Alberta,anada, 1321-1332
Canfield DC, Langeland KA, Maceina MJ, Haller WT, Shireman JV, Jones JR (1983)
Trophic state classification oflakes with aquatic macrophytes, Can. Jour. Fish Aquatic
Science, 40, 1713-18
Canfield DC, Shireman JV, Coole DE, Haller WT, Watkins CE, Maceina MJ (1984)
Prediction of chlorophyll a concentrations in Florida Lakes: Importance of aqautic
macrophytes, Can. Jour. Fish Aquatic Science, 41, 409-501
Demuth H, Beale M (1998) Neural Network Toolbox User's Guide, The MathWorks Inc.,
Natick, MA
Dillon PJ, Rigler FH (1974) The phosphorus-chlorophyll relationship in lakes, Limnol.
Oceanogr., 19, 767-773
Fu LM (1994) Neural Networks for Computer Intelligence, McGraw-Hill, Inc
Hagan MT, Menhaj M (1994) Training feedforward networks with the Marquardt
algorithm, IEEE Trans.on Neural Networks, 5, 989-993
Jorgensen SE (1976) A eutrophication model for a lake, Ecol. Model., 2, 147-162
Kaml C, Soyupak S, Germen E (1998a) A new approach to mathematical water quality
modelling in Reservoirs: Neural Networks, Int. Rev. of Hydrobiol., 83, 689-696
Kaml C, Soyupak S, Güven E, Aydooan A, Alp E (1998b) Limnolojik veri tabano
geliotirilmesi ve TCP/IP üzerinden WWW arayüzü ile ioletilmesi: Keban Baraj Gölü
Veri Tabano Ömeoi, DSo <;:evre Semineri, Fethiye,Turkey (in Turkish)
Kaml C (1999a) Development of An Artificial Neural Network Model for the Estimation of
Chlorophyll-a in Lakes, PhD Thesis, METU, Department of Environmental
Engineering, Ankara, Turkey
Kaml C, Soyupak S, Yurteri C (1999b) Neural network models as a management tool in
lakes, Hydrobiologia, 408-409, 139 - 144
Kaml C, Soyupak S, <;:ilesiz AF, Akbay N, Germen E (2000) Case studies on the use of
neural networks in eutrophication modeling, Ecol. Model. ,134 (2-3), 145 - 152