| 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 | 450 | 1997 |
| Applications of machine learning to ecological modelling F Recknagel Ecological Modelling 146 (1-3), 303-310, 2001 | 317 | 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 | 245 | 1983 |
| 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 | 164 | 2002 |
| ANNA–Artificial Neural Network model for predicting species abundance and succession of blue-green algae F Recknagel Hydrobiologia 349, 47-57, 1997 | 164 | 1997 |
| 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 | 140 | 2016 |
| 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 | 128 | 2001 |
| Ecological informatics: understanding ecology by biologically-inspired computation. 1st Edition F Recknagel Springer Science & Business Media, 2002 | 122 | 2002 |
| 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 | 120 | 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 | 103 | 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 | 103 | 1982 |
| 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 | 82 | 2002 |
| Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation F Recknagel, PT Orr, H Cao Harmful Algae 31, 26-34, 2014 | 75 | 2014 |
| Modelling the impacts of altered management practices, land use and climate changes on the water quality of the Millbrook catchment reservoir system in South Australia HH Nguyen, F Recknagel, W Meyer, J Frizenschaf, M Shrestha Journal of Environmental Management 202, 2017 | 73 | 2017 |
| Prediction and elucidation of population dynamics of the blue-green algaeMicrocyati aeruginosa and the diatom Stephanodiscus hantzschii in the nakdong river-reservoir system … KS Jeong, F Recknagel, GJ Joo Ecological Informatics. Scope, Techniques and Applications, 255-273, 2006 | 73 | 2006 |
| Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials Q Chen, T Guan, L Yun, R Li, F Recknagel Harmful Algae 43, 58-65, 2015 | 72 | 2015 |
| Handbook of ecological modelling and informatics SE Jørgensen, TS Chon, F Recknagel Wit Press, 2009 | 71 | 2009 |
| 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 | 66 | 2007 |
| Future climate and land uses effects on flow and nutrient loads of a Mediterranean catchment in South Australia MK Shrestha, F Recknagel, J Frizenschaf, W Meyer Science of the Total Environment 590, 186-193, 2017 | 65 | 2017 |
| 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 | 61 | 2001 |
