Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France)

International audienceIn southern France, flash flood episodes frequently cause fatalities and severe damage. In order to inform and warn populations, the French flood forecasting service (SCHAPI, Service Central d'Hydrométéorologie et d'Appuià la Prévision des Inondations) initiated the BVNE (Bassin Versant Numérique Expérimental, or Experimental Digital Basin) project in an effort to enhance flash flood predictability. The target area for this study is the Gardon d'Anduze basin, located in the heart of the Cévennes range. In this Mediterranean mountainous setting, rainfall intensity can be very high, resulting in flash flooding. Discharge and rainfall gauges are often exposed to extreme weather conditions , which undermines measurement accuracy and continuity. Moreover, the processes governing rainfall-discharge relations are not well understood for these steeply-sloped and heterogeneous basins. In this context of inadequate information on both the forcing variables and process knowledge , neural networks are investigated due to their universal approximation and parsimony properties. We demonstrate herein that thanks to a rigorous variable and complexity selection , efficient forecasting of up to two-hour durations, without requiring rainfall forecasting as input, can be derived using the measured discharges available from a feedforward model. In the case of discharge gauge malfunction, in degraded mode, forecasting may result using a recurrent neural network model. We also observe that neural network models exhibit low sensitivity to uncertainty in rainfall measurements since producing ensemble forecasting does not significantly affect forecasting quality. In providing good results, this study suggests close consideration of our main purpose: generating forecasting on ungauged basins

Feed-forward vs recurrent neural network models for non-stationarity modelling using data assimilation and adaptivity

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Ensemble model to enhance robustness of flash flood forecasting using an Artificial Neural Network: case-study on the Gardon Basin (south-eastern France)

International audienceDuring the last few decades neural networks have been increasingly used in hydrological modelling for theirfundamental property of parsimony and of universal approximation of non-linear functions. For the purposeof flash flood forecasting, feed-forward and recurrent multi-layer perceptrons appear to be efficient tools.Nevertheless, their forecasting performances are sensitive to the initialization of the network parameters. Wehave studied the cross-validation efficiency to select initialization providing the best forecasts in real time situation.Sensitivity to initialization of feed-forward and recurrent models is compared for one-hour lead-timeforecasts. This study shows that cross-validation is unable to select the best initialization. A more robustmodel has been designed using the median of several models outputs; in this context, this paper analysesthe design of the ensemble model for both recurrent and feed-forward models

Modelo de “ensemble” para incrementar la robusted de la predicción de avenidas utilizando redes neuronales artificiales: aplicación a la cuenca Gardon (sureste de Francia)

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Performance and complementarity of two systemic models (reservoir and neural networks) used to simulate spring discharge and piezometry for a karst aquifer

International audienceKarst aquifers can provide previously untapped freshwaterresourcesand have thus generated considerable interest among stakeholders involved in thewater supplysector.Here we compare the capacity of two systemic models to simulate the discharge and piezometry of a karst aquifer.Systemic models have the advantage of allowing the study of heterogeneous, complex karst systems without relying on extensive geographical and meteorological datasets.The effectiveness and complementarityof the two models are evaluated for a range of hydrologic conditions and for three methods to estimate evapotranspiration (Monteith, a priori ET, and effective rainfall).The first model is a reservoir model (referred to as VENSIM, after the software used), which is designed with just one reservoir so as to be as parsimonious as possible. The second model is a neural network (NN) model. The models are designed to simulate the rainfall-runoff and rainfall-water level relations in a karst conduit.The Lezaquifer, a karst aquifer located near the city of Montpellierin southern France and a critical water resource, was chosen tocompare the two models. Simulated discharge and water level were compared after completing model design and calibration.The results suggest that the NN model is more effective at incorporating the nonlinearity of the karst spring for extreme events (extreme low and high water levels), whereas VENSIM provides a better representation of intermediate-amplitude water level fluctuations.VENSIM is sensitive to the method used to estimate evapotranspiration, whereas the NN model is not. Given that the NN model performs better for extreme events, it is better for operational applications (predicting floods or determiningwater pumping height). VENSIM, on the other hand, seems more appropriate for representing the hydrologic state of the basin during intermediate periods, when severaleffectsare at work: rain, evapotranspiration, developmentofvegetation, etc.A proposal forimprovingboth models is also provided

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

Flash floods pose significant hazards in urbanised zones and have important implications financially and for humans alike in both the present and future due to the likelihood that global climate change will exacerbate their consequences. It is thus of crucial importance to improve the models of these phenomena especially when they occur in heterogeneous and karst basins where they are difficult to describe physically. Toward this goal, this paper applies a recent methodology (Knowledge eXtraction (KnoX) methodology) dedicated to extracting knowledge from a neural network model to better determine the contributions and time responses of several well-identified geographic zones of an aquifer. To assess the interest of this methodology, a case study was conducted in southern France: the Lez hydrosystem whose river crosses the conurbation of Montpellier (400 000 inhabitants). Rainfall contributions and time transfers were estimated and analysed in four geologically delimited zones to estimate the sensitivity of flash floods to water coming from the surface or karst. The Causse de Viols-le-Fort is shown to be the main contributor to flash floods and the delay between surface and underground flooding is estimated to be 3 h. This study will thus help operational flood warning services to better characterise critical rainfall and develop measurements to design efficient flood forecasting models. This generic method can be applied to any basin with sufficient rainfall–run-off measurements

Neural network modeling and geochemical wateranalyses to understand and forecast karst and non-karstpart of flash floods (case study on theLezriver,Southern France)

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The Graduate School WATERS - WATer, Ecosystems, Resources in Societies – in Montpellier

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Les réseaux de neurones artificiels pour la modélisation hydrodynamique des aquifères karstiques.

International audienceLes aquifères karstiques permettent d’alimenter en eau potable près de 25 % de la population mondiale. Sur lepourtour méditerranéen, ils sont considérés parfois comme des châteaux d’eau naturels ou bien comme des barrages écrêteurs de crue. Ces propriétés remarquables, stimulantes par les enjeux soulevés sont cependant difficiles à quantifier et à appréhender objectivement du fait de la complexité du karst, due à une forte hétérogénéité de structure induisant également des non-linéarités. Dans ce contexte cet article propose d’aborder la modélisation hydrodynamique des karsts parune nouvelle approche appartenant à l’apprentissage statistique: les réseaux de neurones artificiels. Ces derniers sont desmodèles mathématiques issus de l’intelligence artificielle qui peuvent identifier toute fonction dynamique non linaire au moyen d’un apprentissage. Ils sont ainsi parfaitement à même de modéliser les fonctions inconnues des aquifères karstiquespourvu que l’on dispose d’une base de données. Très utilisés en hydrologie et appliqués également à la modélisation hydrodynamique des aquifères karstiques, les réseaux de neurones sont tout d’abord abordés dans cet article pour effectuer des fonctions de prévision des crues ou de simulation de débit. Leur capacité d’apprentissage est présentée dans le cadre du paradigme systémique, non seulement pour réaliser la fonction de boîte noire, mais également pour améliorer la connaissance des aquifères karstiques. Ainsi, l’origine des eaux et les temps de transferts peuvent être mieux appréhendés grâce à une méthodologie originale. Cet article présente les études menées sur deux bassins karstiques bien connus, et disposant de nombreuses données de pluie et de débit: le Baget (Ariège) et le Lez (Hérault)