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 Anne Johannet on behalf of the Authors (27 Jul 2015) Manuscript

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

AR by Anne Johannet on behalf of the Authors (29 Sep 2015)

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.