| 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 | 377 | 1997 |
| Applications of machine learning to ecological modelling F Recknagel Ecological Modelling 146 (1-3), 303-310, 2001 | 224 | 2001 |
| 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 | 223 | 1983 |
| Ecological Informatics. Scope, Techniques and Applications F Recknagel Springer, 2006 | 170* | 2006 |
| ANNA–Artificial Neural Network model for predicting species abundance and succession of blue-green algae F Recknagel Hydrobiologia 349 (1), 47-57, 1997 | 140 | 1997 |
| 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 | 137 | 2002 |
| Ecological informatics. Understanding ecology by biologically-inspired computation F Recknagel Springer, 2003 | 114* | 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 | 106 | 2001 |
| 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 | 98 | 1982 |
| 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 | 94 | 2001 |
| 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 | 93 | 2005 |
| Sensitivity analysis C Rate, SR Rate | 84* | 2005 |
| 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 | 65 | 2002 |
| 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 | 59 | 2006 |
| 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 | 58 | 2007 |
| 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 | 55 | 2016 |
| Handbook of ecological modelling and informatics SE Jørgensen, TS Chon, F Recknagel Wit Press, 2009 | 55 | 2009 |
| VALIDATION OF THE ECOLOGICAL SIMULATION MODEL" SALMO" F Recknagel | 55 | 1982 |
| 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 | 54 | 1995 |
| Predicting eutrophication effects in the Burrinjuck Reservoir (Australia) by means of the deterministic model SALMO and the recurrent neural network model ANNA M Walter, F Recknagel, C Carpenter, M Bormans Ecological Modelling 146 (1-3), 97-113, 2001 | 53 | 2001 |
