Articles | Volume 7, issue 5
https://doi.org/10.5194/hess-7-693-2003
© Author(s) 2003. 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-7-693-2003
© Author(s) 2003. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology?
E. Gaume
Ecole Nationale des Ponts et Chaussées, CEREVE, 6 et 8 avenue Blaise Pascal, 77455 Marne la Vallée Cedex 2, France
Email for corresponding author: gaume@cereve.enpc.fr
Ecole Nationale des Ponts et Chaussées, CEREVE, 6 et 8 avenue Blaise Pascal, 77455 Marne la Vallée Cedex 2, France
Email for corresponding author: gaume@cereve.enpc.fr
R. Gosset
Ecole Nationale des Ponts et Chaussées, CEREVE, 6 et 8 avenue Blaise Pascal, 77455 Marne la Vallée Cedex 2, France
Email for corresponding author: gaume@cereve.enpc.fr
Viewed
Total article views: 2,123 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
1,071 | 948 | 104 | 2,123 | 109 | 94 |
- HTML: 1,071
- PDF: 948
- XML: 104
- Total: 2,123
- BibTeX: 109
- EndNote: 94
Cited
49 citations as recorded by crossref.
- A generic and practical wave overtopping model that includes uncertainty T. Pullen et al. 10.1680/jmaen.2017.31
- Development of a Stepwise-Clustered Hydrological Inference Model Z. Li et al. 10.1061/(ASCE)HE.1943-5584.0001165
- Value of process understanding in the era of machine learning: A case for recession flow prediction P. Istalkar et al. 10.1016/j.jhydrol.2023.130350
- Runoff forecasting by artificial neural network and conventional model A. Ghumman et al. 10.1016/j.aej.2012.01.005
- Coupled Model for Assessing the Present and Future Watershed Vulnerabilities to Climate Change Impacts A. Martínez et al. 10.3390/w15040711
- Estimation of parameters of the transient storage model by means of multi-layer perceptron neural networks / Estimation des paramètres du modèle de transport TSM au moyen de réseaux de neurones perceptrons multi-couches P. ROWIŃSKI & A. PIOTROWSKI 10.1623/hysj.53.1.165
- SUITABILITY OF NEURAL NETWORK TECHNIQUES FOR RAINFALL-RUNOFF MODELLING OVER A RIVER BASIN: A COMPREHENSIVE LITERATURE REVIEW K. SANJEEV et al. 10.26634/jfet.13.4.14434
- Comparing various artificial neural network types for water temperature prediction in rivers A. Piotrowski et al. 10.1016/j.jhydrol.2015.07.044
- Comparative Analysis of Training Methods and Different Data for the Rainfall-Runoff Predication Using Artificial Neural Networks K. Solaimani & Z. Darvari 10.3923/rjes.2008.353.365
- Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada Z. Li et al. 10.1016/j.scitotenv.2016.01.002
- A Bayesian Hierarchical Network Model for Daily Streamflow Ensemble Forecasting Á. Ossandón et al. 10.1029/2021WR029920
- A Statistical Hydrological Model for Yangtze River Watershed Based on Stepwise Cluster Analysis F. Wang et al. 10.3389/feart.2021.742331
- Application of artificial neural networks to X‐ray fluorescence spectrum analysis F. Li et al. 10.1002/xrs.2996
- Artificial Neural Network and Multiple Linear Regression for Flood Prediction in Mohawk River, New York K. Tsakiri et al. 10.3390/w10091158
- Prediction of side weir discharge coefficient by support vector machine technique H. Azamathulla et al. 10.2166/ws.2016.014
- Embedding machine learning techniques into a conceptual model to improve monthly runoff simulation: A nested hybrid rainfall-runoff modeling U. Okkan et al. 10.1016/j.jhydrol.2021.126433
- Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds J. Shortridge et al. 10.5194/hess-20-2611-2016
- Daily Stream Flow Prediction Capability of Artificial Neural Networks as influenced by Minimum Air Temperature Data M. Nayebi et al. 10.1016/j.biosystemseng.2006.08.012
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
- On the reconstruction of Ito models on the basis of time series with long-tail distributions A. Rozmarynowska 10.2478/s11600-008-0074-2
- Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions H. Maier et al. 10.1016/j.envsoft.2010.02.003
- ANN-SCS-based hybrid model in conjunction with GCM to evaluate the impact of climate change on the flow scenario of the River Subansiri S. Barman & R. Bhattacharjya 10.2166/wcc.2019.221
- Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
- Multi-objective performance comparison of an artificial neural network and a conceptual rainfall—runoff model N. DE VOS & T. RIENTJES 10.1623/hysj.52.3.397
- Comparison of performance of twelve monthly water balance models in different climatic catchments of China P. Bai et al. 10.1016/j.jhydrol.2015.09.015
- Ensemble Based Forecasting and Optimization Framework to Optimize Releases from Water Supply Reservoirs for Flood Control V. Ramaswamy & F. Saleh 10.1007/s11269-019-02481-8
- Letter to the Editor on “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” by Ozgur Kisi & Jalal Shiri [Water Resources Management 25 (2011) 3135–3152] D. Beriro et al. 10.1007/s11269-012-0049-6
- Comparison of an artificial neural network and a conceptual rainfall–runoff model in the simulation of ephemeral streamflow I. Daliakopoulos & I. Tsanis 10.1080/02626667.2016.1154151
- Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling E. Toth & A. Brath 10.1029/2006WR005383
- A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2012.10.019
- The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models M. Demirel et al. 10.5194/hess-19-275-2015
- Product-Units neural networks for catchment runoff forecasting A. Piotrowski & J. Napiorkowski 10.1016/j.advwatres.2012.05.016
- Prediction and Sensitivity Analysis of the Cetane Number of Different Biodiesel Fuels Using an Artificial Neural Network S. Hao et al. 10.1021/acs.energyfuels.1c01957
- The effects of online-training artificial neural network mechanism and multi-stage parametric modeling method on simulation-based design system for ship optimization L. Du et al. 10.1016/j.oceaneng.2024.118284
- Multiobjective training of artificial neural networks for rainfall‐runoff modeling N. de Vos & T. Rientjes 10.1029/2007WR006734
- Neural network modelling of non-linear hydrological relationships R. Abrahart & L. See 10.5194/hess-11-1563-2007
- Efficient solution of linear inverse problems using an iterative linear neural network with a generalization training approach Q. Tieng et al. 10.1088/2399-6528/abebcf
- A step towards considering the spatial heterogeneity of urban key features in urban hydrology flood modelling J. Leandro et al. 10.1016/j.jhydrol.2016.01.060
- Estimation of the Budyko model parameter for small basins in China P. Bai et al. 10.1002/hyp.13577
- Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks G. Nearing et al. 10.5194/hess-26-5493-2022
- Current awareness 10.1002/hyp.5823
- Fugacity modelling of the fate of micropollutants in aqueous systems — Uncertainty and sensitivity issues Y. Wang et al. 10.1016/j.scitotenv.2019.134249
- Indian summer monsoon rainfall prediction using artificial neural network P. Singh & B. Borah 10.1007/s00477-013-0695-0
- Stream flow forecasting by artificial neural network (ANN) model trained by real coded genetic algorithm (GA) S. A et al. 10.5917/jagh1987.48.233
- Optimization of the generalization capability for rainfall–runoff modeling by neural networks: the case of the Lez aquifer (southern France) L. Kong A Siou et al. 10.1007/s12665-011-1450-9
- Study of teleconnection between hydrological variables and climatological variables in a headwater basin of the Maipo River for forecast model application J. Montalva et al. 10.24850/j-tyca-16-4-3
- Discussion of “River flow estimation from upstream flow records by artificial intelligence methods” by M.E. Turan, M.A. Yurdusev [J. Hydrol. 369 (2009) 71–77] N. Mount & R. Abrahart 10.1016/j.jhydrol.2010.11.004
- HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts C. Dawson et al. 10.1016/j.envsoft.2006.06.008
- One-Day-Ahead Streamflow Forecasting Using Artificial Neural Networks and a Meteorological Mesoscale Model A. Linares-Rodriguez et al. 10.1061/(ASCE)HE.1943-5584.0001163
Latest update: 14 Dec 2024