Articles | Volume 17, issue 3
https://doi.org/10.5194/hess-17-935-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-17-935-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
05 Mar 2013
Research article |
| 05 Mar 2013
Online multistep-ahead inundation depth forecasts by recurrent NARX networks
H.-Y. Shen
Department of Water Resources and Environmental Engineering, Tamkang University, Tamsui, Taiwan
L.-C. Chang
Department of Water Resources and Environmental Engineering, Tamkang University, Tamsui, Taiwan
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- Nonlinear autoregressive neural networks with external inputs for forecasting of typhoon inundation level H. Ouyang 10.1007/s10661-017-6100-6
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- Application of Nonlinear Time Series and Machine Learning Algorithms for Forecasting Groundwater Flooding in a Lowland Karst Area B. Basu et al. 10.1029/2021WR029576
- Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models P. Pishgah Hadiyan et al. 10.1007/s11269-022-03170-9
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- On the Downscaling of Meteorological Fields Using Recurrent Networks for Modelling the Water Balance in a Meso-Scale Catchment Area of Saxony, Germany R. Kronenberg et al. 10.4236/acs.2013.34058
- Data-Driven Approach for the Rapid Simulation of Urban Flood Prediction H. Kim & K. Han 10.1007/s12205-020-1304-7
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- An artificial neural network-based snow cover predictive modeling in the higher Himalayas B. Mishra et al. 10.1007/s11629-014-2985-5
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- Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting Y. Tian et al. 10.3390/w10111655
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- Regional flood inundation nowcast using hybrid SOM and dynamic neural networks L. Chang et al. 10.1016/j.jhydrol.2014.07.036
- Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias Y. Liang et al. 10.1109/ACCESS.2021.3053265
- Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network Y. Zhou et al. 10.3390/w12020578
- A machine learning approach for forecasting and visualising flood inundation information S. Kabir et al. 10.1680/jwama.20.00002
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