Articles | Volume 11, issue 3
https://doi.org/10.5194/nhess-11-771-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-11-771-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database
T.-Y. Pan
Center for Weather Climate and Disaster Research, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
J.-S. Lai
Hydrotech Research Institute, National Taiwan University, Taipei 10617, Taiwan
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Center for Weather Climate and Disaster Research, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
T.-J. Chang
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Ecological Engineering Research Center, National Taiwan University, Taipei 10617, Taiwan
Center for Weather Climate and Disaster Research, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
H.-K. Chang
Hydrotech Research Institute, National Taiwan University, Taipei 10617, Taiwan
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
K.-C. Chang
Water Resources Agency, Ministry of Economic Affairs, 41-3 Sec. 3, Hsinyi Rd., Taipei 10651, Taiwan
Y.-C. Tan
Hydrotech Research Institute, National Taiwan University, Taipei 10617, Taiwan
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
Center for Weather Climate and Disaster Research, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
Viewed
Total article views: 2,066 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 01 Feb 2013)
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
890 | 1,011 | 165 | 2,066 | 98 | 86 |
- HTML: 890
- PDF: 1,011
- XML: 165
- Total: 2,066
- BibTeX: 98
- EndNote: 86
Cited
22 citations as recorded by crossref.
- Improvement of a drainage system for flood management with assessment of the potential effects of climate change H. Chang et al. 10.1080/02626667.2013.836276
- An integrated two-stage support vector machine approach to forecast inundation maps during typhoons B. Jhong et al. 10.1016/j.jhydrol.2017.01.057
- Scenario-Based Real-Time Flood Prediction with Logistic Regression J. Lee & B. Kim 10.3390/w13091191
- Effective real-time forecasting of inundation maps for early warning systems during typhoons J. Wang et al. 10.1051/matecconf/201814703014
- Real-Time Urban Inundation Prediction Combining Hydraulic and Probabilistic Methods H. Kim et al. 10.3390/w11020293
- Assessing the effective spatial characteristics of input features through physics-informed machine learning models in inundation forecasting during typhoons B. Jhong et al. 10.1080/02626667.2022.2092406
- Regional flood inundation nowcast using hybrid SOM and dynamic neural networks L. Chang et al. 10.1016/j.jhydrol.2014.07.036
- Flash flood warnings using the ensemble precipitation forecasting technique: A case study on forecasting floods in Taiwan caused by typhoons T. Yang et al. 10.1016/j.jhydrol.2014.11.028
- Real-Time Flood Disaster Prediction System by Applying Machine Learning Technique H. Keum et al. 10.1007/s12205-020-1677-7
- A study of road closure due to rainfall and flood zone based on logistic regression H. Zhong & D. Liang 10.1016/j.ijdrr.2024.104291
- Improving the Long Lead-Time Inundation Forecasts Using Effective Typhoon Characteristics B. Jhong et al. 10.1007/s11269-016-1418-3
- Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm H. Ouyang 10.5194/nhess-16-1897-2016
- Ensemble Learning Technology for Coastal Flood Forecasting in Internet-of-Things-Enabled Smart City W. Dai et al. 10.1007/s44196-021-00023-y
- A machine learning approach for forecasting and visualising flood inundation information S. Kabir et al. 10.1680/jwama.20.00002
- A Sink Screening Approach for 1D Surface Network Simplification in Urban Flood Modelling G. Zhao et al. 10.3390/w14060963
- Online multistep-ahead inundation depth forecasts by recurrent NARX networks H. Shen & L. Chang 10.5194/hess-17-935-2013
- A Support Vector Machine Forecasting Model for Typhoon Flood Inundation Mapping and Early Flood Warning Systems M. Chang et al. 10.3390/w10121734
- Rapid prediction of flood inundation by interpolation between flood library maps for real-time applications W. Wang et al. 10.1016/j.jhydrol.2022.127735
- A probabilistic pluvial flood warning model based on nest som using radar reflectivity data T. Pan et al. 10.1007/s00477-024-02867-0
- A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands V. Gholami & M. Khaleghi 10.17221/90/2020-JFS
- Coupling typhoon rainfall forecasting with overland-flow modeling for early warning of inundation T. Pan et al. 10.1007/s11069-011-0061-9
- Pluvial flooding: High-resolution stochastic hazard mapping in urban areas by using fast-processing DEM-based algorithms L. Mediero et al. 10.1016/j.jhydrol.2022.127649
Latest update: 21 Jan 2025