Chapter 13 . Cross-Sectional Estimation of Chlorophyll a in Lakes 263
Keiner LE, Brown CW (1998) A neural network as a non-linear chlorophyll estimation
algorithm",
http://orbit19i/nesdis/noaa.gov/-Ikeiner/seanam neural!
seabam2.htm
Moreau Y, Louies S, Vandewalle J, Brenig L (1999) Embedding recurrent neural
networks into predator-prey models, Neural Networks, 12,237-245.
Rast W, Lee GF (1978) Summary Analysis of the North American Project (US Portion)
OECD Eutrophication Project: Nutrient Loading-Lake Response Relationships and
Trophic State Indices, USEPA Corvallis Environmental Research Laboratory ,
Corvallis, OR,EPA-600/3-78-008.
Recknagel F, Petzoldt T, Jacke 0, Krusche F (1994) Hybrid expert system DELAQUA: a
toolkit for water quality control of lakes and reservoirs, Ecol. Model., 71, 17-36.
Recknagel F, French M, Harkonen P, Yabunaka K (1997) Artificial neural network
approach for modelling and prediction of algal blooms, Ecol. Model., 96, 11-28.
Recknagel F (1997) ANNA - Artificial Neural Network model predicting species
abundance and succession ofblue-green Algae. Hydrobiologia 349,47-57.
Robertson SG, Morison AK (1999) A trial of artificial neural networks for automatically
estimating the age of fish, Mar. Freshwater Res., 50, 73-82.
Ryding S, Rast W (1989) The Control of Eutrophication of Lakes and Reservoirs,
Parthenon Publishing Co., UNESCO.
Scardi M (1996) Artificial neural networks as empirical models for estimating
phytoplankton production, Mar. Ecol. Ser., 139,289-299.
Smith VH, Shapiro J (1981) A Retrospective Look at the Effects of Phosphorus Removal in
Lakes,in Restoration of Lakes and Inland Waters, USEPA, Office of Water Regulations
and Standards, Washington, DC, EPA-440/5-81-01O.
Soyupak S, Yemioen D, Mukhallalati L, Erdem S, Akbay N, Yerli S (1998) The spatial and
temporal variability of limnological properties of a very large and deep reservoir, Journal
of Intemational Review ofHydrobiology, 83,183-190.
Vollenweider RA, Kerekes JJ (1981) Background and Summary Results of the OECD
Cooperative Program on Eutrophication , Int. Symp. on Inland Waters and Lake
Restoration, U.S. EPA, Washington D.C., 25-36.
Whitehead PG, Hornberger GM (1984) Modelling algal behavior in the River Tharnes,
Wat. Res., 18(8),945-953.
Yabunaka K, Hosomi M, Murakarni A (1997) Novel application of a back-propagation
artificial neural network model formulated to predict algal bloom, Water science and
Technology., 36, 89-97.
Yemioen D (1994) Keban Baraj Gölü ve Havzaso <;evre Sorunlaro Projesi - Final Raporu -
Keban Baraj Gölü Sularonon Fiziksel, Kimyasal ve Biyolojik Özellikleri-Keban'da
Ötrofikasyon-Hidrodinarnik Modelleme Sonu9laro ve Su Kalitesi Modelleme
Sonu9laro- <;özüm Önerileri, TÜBoTAK DEBAG 124/G Projesi, ODTÜ-DSo Genel
Müdürlü°ü ve DSo 9. Böige, Elaz", Turkey(in Turkish).
Zhang Q, Stanley SJ (1997) The artificial neural network modelling approach for water
demand forecasting in Edmonton, CSCEIASCE Env. Eng. Conf. Proceedings,
Edmonton, Alberta, Canada, 1333-1344.
Keiner LE, Brown CW (1998) A neural network as a non-linear chlorophyll estimation
algorithm",
http://orbit19i/nesdis/noaa.gov/-Ikeiner/seanam neural!
seabam2.htm
Moreau Y, Louies S, Vandewalle J, Brenig L (1999) Embedding recurrent neural
networks into predator-prey models, Neural Networks, 12,237-245.
Rast W, Lee GF (1978) Summary Analysis of the North American Project (US Portion)
OECD Eutrophication Project: Nutrient Loading-Lake Response Relationships and
Trophic State Indices, USEPA Corvallis Environmental Research Laboratory ,
Corvallis, OR,EPA-600/3-78-008.
Recknagel F, Petzoldt T, Jacke 0, Krusche F (1994) Hybrid expert system DELAQUA: a
toolkit for water quality control of lakes and reservoirs, Ecol. Model., 71, 17-36.
Recknagel F, French M, Harkonen P, Yabunaka K (1997) Artificial neural network
approach for modelling and prediction of algal blooms, Ecol. Model., 96, 11-28.
Recknagel F (1997) ANNA - Artificial Neural Network model predicting species
abundance and succession ofblue-green Algae. Hydrobiologia 349,47-57.
Robertson SG, Morison AK (1999) A trial of artificial neural networks for automatically
estimating the age of fish, Mar. Freshwater Res., 50, 73-82.
Ryding S, Rast W (1989) The Control of Eutrophication of Lakes and Reservoirs,
Parthenon Publishing Co., UNESCO.
Scardi M (1996) Artificial neural networks as empirical models for estimating
phytoplankton production, Mar. Ecol. Ser., 139,289-299.
Smith VH, Shapiro J (1981) A Retrospective Look at the Effects of Phosphorus Removal in
Lakes,in Restoration of Lakes and Inland Waters, USEPA, Office of Water Regulations
and Standards, Washington, DC, EPA-440/5-81-01O.
Soyupak S, Yemioen D, Mukhallalati L, Erdem S, Akbay N, Yerli S (1998) The spatial and
temporal variability of limnological properties of a very large and deep reservoir, Journal
of Intemational Review ofHydrobiology, 83,183-190.
Vollenweider RA, Kerekes JJ (1981) Background and Summary Results of the OECD
Cooperative Program on Eutrophication , Int. Symp. on Inland Waters and Lake
Restoration, U.S. EPA, Washington D.C., 25-36.
Whitehead PG, Hornberger GM (1984) Modelling algal behavior in the River Tharnes,
Wat. Res., 18(8),945-953.
Yabunaka K, Hosomi M, Murakarni A (1997) Novel application of a back-propagation
artificial neural network model formulated to predict algal bloom, Water science and
Technology., 36, 89-97.
Yemioen D (1994) Keban Baraj Gölü ve Havzaso <;evre Sorunlaro Projesi - Final Raporu -
Keban Baraj Gölü Sularonon Fiziksel, Kimyasal ve Biyolojik Özellikleri-Keban'da
Ötrofikasyon-Hidrodinarnik Modelleme Sonu9laro ve Su Kalitesi Modelleme
Sonu9laro- <;özüm Önerileri, TÜBoTAK DEBAG 124/G Projesi, ODTÜ-DSo Genel
Müdürlü°ü ve DSo 9. Böige, Elaz", Turkey(in Turkish).
Zhang Q, Stanley SJ (1997) The artificial neural network modelling approach for water
demand forecasting in Edmonton, CSCEIASCE Env. Eng. Conf. Proceedings,
Edmonton, Alberta, Canada, 1333-1344.