Friedrich Recknagel
Titel
Zitiert von
Zitiert von
Jahr
Artificial neural network approach for modelling and prediction of algal blooms
F Recknagel, M French, P Harkonen, KI Yabunaka
Ecological Modelling 96 (1-3), 11-28, 1997
3681997
Calcite precipitation as a natural control mechanism of eutrophication
R Koschel, J Benndorf, G Proft, F Recknagel
Archiv für Hydrobiologie 98 (3), 380-408, 1983
2191983
Applications of machine learning to ecological modelling
F Recknagel
Ecological Modelling 146 (1-3), 303-310, 2001
2152001
Ecological Informatics. Scope, Techniques and Applications
F Recknagel
Springer, 2006
168*2006
In situ removal of dissolved phosphorus in irrigation drainage water by planted floats: preliminary results from growth chamber experiment
L Wen, F Recknagel
Agriculture, ecosystems & environment 90 (1), 9-15, 2002
1422002
ANNA–Artificial Neural Network model for predicting species abundance and succession of blue-green algae
F Recknagel
Hydrobiologia 349 (1-3), 47-57, 1997
1391997
Ecological informatics. Understanding ecology by biologically-inspired computation
F Recknagel
Springer, 2003
113*2003
Prediction and elucidation of phytoplankton dynamics in the Nakdong River (Korea) by means of a recurrent artificial neural network
KS Jeong, GJ Joo, HW Kim, K Ha, F Recknagel
Ecological Modelling 146 (1-3), 115-129, 2001
1052001
Problems of application of the ecological model SALMO to lakes and reservoirs having various trophic states
J Benndorf, F Recknagel
Ecological Modelling 17 (2), 129-145, 1982
961982
Response of stream macroinvertebrates to changes in salinity and the development of a salinity index
N Horrigan, S Choy, J Marshall, F Recknagel
Marine and Freshwater Research 56 (6), 825-833, 2005
932005
Sensitivity analysis
C Rate, SR Rate
89*2005
Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes
H Wilson, F Recknagel
Ecological Modelling 146 (1-3), 69-84, 2001
892001
Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling of algal blooms in freshwater lakes
F Recknagel, J Bobbin, P Whigham, H Wilson
Journal of Hydroinformatics 4 (2), 125-133, 2002
642002
Prediction and elucidation of population dynamics of the blue-green algae Microcystis aeruginosa and the diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System …
KS Jeong, F Recknagel, GJ Joo
Ecological Informatics, 255-273, 2006
602006
Handbook of ecological modelling and informatics
SE Jørgensen, TS Chon, F Recknagel
Wit Press, 2009
572009
Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms
WS Chan, F Recknagel, H Cao, HD Park
Water Research 41 (10), 2247-2255, 2007
562007
Predicting chlorophyll-a in freshwater lakes by hybridising process-based models and genetic algorithms
PA Whigham, F Recknagel
Ecological Modelling 146 (1-3), 243-251, 2001
532001
Short-and long-term control of external and internal phosphorus loads in lakes—a scenario analysis
F Recknagel, M Hosomi, T Fukushima, DS Kong
Water Research 29 (7), 1767-1779, 1995
531995
VALIDATION OF THE ECOLOGICAL SIMULATION MODEL" SALMO"
F Recknagel
531982
Assessing SWAT models based on single and multi-site calibration for the simulation of flow and nutrient loads in the semi-arid Onkaparinga catchment in South Australia
MK Shrestha, F Recknagel, J Frizenschaf, W Meyer
Agricultural Water Management 175, 61-71, 2016
502016
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