Articles | Volume 10, issue 4
https://doi.org/10.5194/hess-10-603-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
https://doi.org/10.5194/hess-10-603-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
07 Sep 2006
Optimising training data for ANNs with Genetic Algorithms
R. G. Kamp
Section of Water Resources, Delft University of Technology, Delft, The Netherlands
MX.Systems B.V., Rijswijk, The Netherlands
H. H. G. Savenije
Section of Water Resources, Delft University of Technology, Delft, The Netherlands
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- Comment about lack of sufficient data on “A prediction model for finding the optimal laser parameters in additive manufacturing of NiTi shape memory alloy” by Mehrpouya et al. [The International Journal of Advanced Manufacturing Technology 105.11 (2019): 4691–4699.] O. Mohamed & W. Xu 10.1007/s40964-021-00236-9
- GENETIC ALGORITHM APPLICATIONS IN WATER RESOURCES S. Mohan & D. Vijayalakshmi 10.1080/09715010.2009.10514971
- A Hybrid Genetic Algorithm and Back-Propagation Classifier for Gearbox Fault Diagnosis S. Tyagi & S. Panigrahi 10.1080/08839514.2017.1413066
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- Hydrological model coupling with ANNs R. Kamp & H. Savenije 10.5194/hess-11-1869-2007
- An Adaptive Time-delay Neural Network Training using Parallel Genetic Algorithms in Time-series Prediction and Classification A. Ourdighi & A. Benyettou 10.3923/jas.2010.2115.2120
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
- System performances analysis of reservoir optimization–simulation model in application of artificial bee colony algorithm M. Hossain et al. 10.1007/s00521-016-2798-2
- Hydrodynamic characteristics prediction models for water-cooled wall under different loads based on Artificial neural network J. Yang et al. 10.1016/j.applthermaleng.2024.125284
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- Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm P. Eslami et al. 10.1057/mel.2016.1
- Estimation of Nash's IUH parameters using stochastic search algorithms S. Mohan & D. Vijayalakshmi 10.1002/hyp.6954
4 citations as recorded by crossref.
- Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece P. Nastos et al. 10.1016/j.atmosres.2013.11.013
- Developing a hybrid multi‐model for peak flood forecasting Y. Chidthong et al. 10.1002/hyp.7307
- A self-tuning ANN model for simulation and forecasting of surface flows O. Bozorg-Haddad et al. 10.1007/s11269-016-1301-2
- Evaluation of coupled ANN-GA model to prioritize flood source areas in ungauged watersheds N. Dehghanian et al. 10.2166/nh.2020.141
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