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: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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Latest update: 24 Jan 2026

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.