Identification of spatial and temporal contributions of rainfalls to flash  floods using neural network modelling: case study on the Lez basin (southern  France)

Darras, T.; Borrell Estupina, V.; Kong-A-Siou, L.; Vayssade, B.; Johannet, A.; Pistre, S.

doi:https://doi.org/10.5194/hess-19-4397-2015

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.

https://doi.org/10.5194/hess-19-4397-2015

© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 19, issue 10

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, V. Borrell Estupina, L. Kong-A-Siou, B. Vayssade, A. Johannet, and S. Pistre

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Final revised paper (published on 30 Oct 2015)
Preprint (discussion started on 08 Apr 2015)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC C1346: 'Review - Darras et al. Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on Lez Basin (Southern France)', Anonymous Referee #1, 06 May 2015
- AC C1725: 'Interactive comment on “Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez Basin (Southern France)” by T. Darras et al. Anonymous Referee #1', Anne Johannet, 23 May 2015
RC C1413: 'Interactive comment on “Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling” by T. Darras et al.', Anonymous Referee #2, 11 May 2015
- AC C1719: 'Interactive comment on “Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez Basin (Southern France)” by T. Darras et al. Anonymous Referee #2', Anne Johannet, 23 May 2015
AC C1659: 'Interactive response to Anonymous Referee #1', Anne Johannet, 21 May 2015

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (16 Jun 2015) by Thomas Kjeldsen

AR by Anna Mirena Feist-Polner on behalf of the Authors (28 Jul 2015) Author's response

ED: Reconsider after major revisions (08 Sep 2015) by Thomas Kjeldsen

ED: Referee Nomination & Report Request started (08 Sep 2015) by Thomas Kjeldsen

RR by Anonymous Referee #2 (23 Sep 2015)

ED: Publish as is (29 Sep 2015) by Thomas Kjeldsen

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.