Articles | Volume 19, issue 10
https://doi.org/10.5194/hess-19-4397-2015
© Author(s) 2015. 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-19-4397-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
30 Oct 2015
Research article |
| 30 Oct 2015
Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France)
T. Darras
École des mines d'Alès, LGEI, 6 avenue de Clavières, 30319 Alès CEDEX, France
Université Montpellier – Hydrosciences Montpellier, MSE, 2 Place Eugène Bataillon, 34095 Montpellier CEDEX 5, France
V. Borrell Estupina
Université Montpellier – Hydrosciences Montpellier, MSE, 2 Place Eugène Bataillon, 34095 Montpellier CEDEX 5, France
L. Kong-A-Siou
MAYANE, 173 chemin de Fescau, 34980 Montferrier-sur-Lez, France
B. Vayssade
École des mines d'Alès, LGEI, 6 avenue de Clavières, 30319 Alès CEDEX, France
A. Johannet
CORRESPONDING AUTHOR
École des mines d'Alès, LGEI, 6 avenue de Clavières, 30319 Alès CEDEX, France
S. Pistre
Université Montpellier – Hydrosciences Montpellier, MSE, 2 Place Eugène Bataillon, 34095 Montpellier CEDEX 5, France
Viewed
Total article views: 2,808 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Apr 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,380 | 1,331 | 97 | 2,808 | 106 | 108 |
- HTML: 1,380
- PDF: 1,331
- XML: 97
- Total: 2,808
- BibTeX: 106
- EndNote: 108
Total article views: 2,053 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Oct 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,074 | 894 | 85 | 2,053 | 94 | 95 |
- HTML: 1,074
- PDF: 894
- XML: 85
- Total: 2,053
- BibTeX: 94
- EndNote: 95
Total article views: 755 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 08 Apr 2015)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 306 | 437 | 12 | 755 | 145 | 12 | 13 |
- HTML: 306
- PDF: 437
- XML: 12
- Total: 755
- Supplement: 145
- BibTeX: 12
- EndNote: 13
Cited
18 citations as recorded by crossref.
- Sub-daily precipitation-streamflow modelling of the karst-dominated basin using an improved grid-based distributed Xinanjiang hydrological model W. Yang et al. 10.1016/j.ejrh.2022.101125
- Hydrological modelling of large-scale karst-dominated basin using a grid-based distributed karst hydrological model L. Chen et al. 10.1016/j.jhydrol.2023.130459
- Flood Stage Forecasting Using Machine-Learning Methods: A Case Study on the Parma River (Italy) S. Dazzi et al. 10.3390/w13121612
- Investigating sea‐state effects on flash flood hydrograph and inundation forecasting G. Papaioannou et al. 10.1002/hyp.14151
- Impacts of rainfall spatial and temporal variabilities on runoff quality and quantity at the watershed scale W. Zhou et al. 10.1016/j.jhydrol.2021.127057
- Evaluating the Forecast Skill of a Hydrometeorological Modelling System in Greece G. Varlas et al. 10.3390/atmos12070902
- A new simple statistical method for the unsupervised clustering of the hydrodynamic behavior at different boreholes: analysis of the obtained clusters in relation to geological knowledge M. Erguy et al. 10.1007/s12665-023-11066-z
- Geomorphology of the Mirador-Calakmul Karst Basin: A GIS-based approach to hydrogeologic mapping R. Ensley et al. 10.1371/journal.pone.0255496
- Emergence of heavy tails in streamflow distributions: the role of spatial rainfall variability H. Wang et al. 10.1016/j.advwatres.2022.104359
- Prediction of monthly regional groundwater levels through hybrid soft-computing techniques F. Chang et al. 10.1016/j.jhydrol.2016.08.006
- Using radar-based quantitative precipitation data with coupled soil- and groundwater balance models for stream flow simulation in a karst area P. Knöll et al. 10.1016/j.jhydrol.2020.124884
- Karst-aquifer operational turbidity forecasting by neural networks and the role of complexity in designing the model: a case study of the Yport basin in Normandy (France) M. Savary et al. 10.1007/s10040-020-02277-w
- Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS D. Tien Bui et al. 10.1016/j.jhydrol.2016.06.027
- Hydrological Forecasting Using Artificial Intelligence Techniques 研. 周 10.12677/JWRR.2019.81001
- Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. 10.5194/hess-26-2405-2022
- The Impact of Rainfall Space‐Time Structure in Flood Frequency Analysis Z. Zhu et al. 10.1029/2018WR023550
- Artificial Intelligence (AI) Studies in Water Resources M. AY & S. ÖZYILDIRIM 10.28978/nesciences.424674
- Karst modelling challenge 1: Results of hydrological modelling P. Jeannin et al. 10.1016/j.jhydrol.2021.126508
18 citations as recorded by crossref.
- Sub-daily precipitation-streamflow modelling of the karst-dominated basin using an improved grid-based distributed Xinanjiang hydrological model W. Yang et al. 10.1016/j.ejrh.2022.101125
- Hydrological modelling of large-scale karst-dominated basin using a grid-based distributed karst hydrological model L. Chen et al. 10.1016/j.jhydrol.2023.130459
- Flood Stage Forecasting Using Machine-Learning Methods: A Case Study on the Parma River (Italy) S. Dazzi et al. 10.3390/w13121612
- Investigating sea‐state effects on flash flood hydrograph and inundation forecasting G. Papaioannou et al. 10.1002/hyp.14151
- Impacts of rainfall spatial and temporal variabilities on runoff quality and quantity at the watershed scale W. Zhou et al. 10.1016/j.jhydrol.2021.127057
- Evaluating the Forecast Skill of a Hydrometeorological Modelling System in Greece G. Varlas et al. 10.3390/atmos12070902
- A new simple statistical method for the unsupervised clustering of the hydrodynamic behavior at different boreholes: analysis of the obtained clusters in relation to geological knowledge M. Erguy et al. 10.1007/s12665-023-11066-z
- Geomorphology of the Mirador-Calakmul Karst Basin: A GIS-based approach to hydrogeologic mapping R. Ensley et al. 10.1371/journal.pone.0255496
- Emergence of heavy tails in streamflow distributions: the role of spatial rainfall variability H. Wang et al. 10.1016/j.advwatres.2022.104359
- Prediction of monthly regional groundwater levels through hybrid soft-computing techniques F. Chang et al. 10.1016/j.jhydrol.2016.08.006
- Using radar-based quantitative precipitation data with coupled soil- and groundwater balance models for stream flow simulation in a karst area P. Knöll et al. 10.1016/j.jhydrol.2020.124884
- Karst-aquifer operational turbidity forecasting by neural networks and the role of complexity in designing the model: a case study of the Yport basin in Normandy (France) M. Savary et al. 10.1007/s10040-020-02277-w
- Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS D. Tien Bui et al. 10.1016/j.jhydrol.2016.06.027
- Hydrological Forecasting Using Artificial Intelligence Techniques 研. 周 10.12677/JWRR.2019.81001
- Karst spring discharge modeling based on deep learning using spatially distributed input data A. Wunsch et al. 10.5194/hess-26-2405-2022
- The Impact of Rainfall Space‐Time Structure in Flood Frequency Analysis Z. Zhu et al. 10.1029/2018WR023550
- Artificial Intelligence (AI) Studies in Water Resources M. AY & S. ÖZYILDIRIM 10.28978/nesciences.424674
- Karst modelling challenge 1: Results of hydrological modelling P. Jeannin et al. 10.1016/j.jhydrol.2021.126508
Saved (final revised paper)
Saved (preprint)
Latest update: 24 Apr 2025
Short summary
Flash floods are important hazards in urbanised zone and constitute important human and financial stakes. This paper applies a novel methodology, KnoX, dedicated to extract knowledge from a neural network model. It was shown that KnoX method could help to better characterize processes of both surface and underground floods.
A case study is chosen in France: the Lez karst hydrosystem whose river crosses the city of Montpellier (400 000 inhabitants). Results will help flood warning services.
Flash floods are important hazards in urbanised zone and constitute important human and...
