Flash Floods Forecasting in a Karstic Basin Using Neural Networks: the Case of the Lez Basin (South of France)

International audienceThe present study focuses on the modeling of the Lez karstic system (France) using artificial neural networks. Two methods of variable selection were compared: cross-correlation and cross-validation. In both cases, the artificial neural network forecasts closely matched the measured discharge, giving Nash criteria higher than 0.8, which can thus provide satisfactory 2-day forecasts

Deep Multilayer Perceptron for Knowledge Extraction: Understanding the Gardon de Mialet Flash Floods Modeling

Issu de : ITISE 2019 - International Conference on Time Series and Forecasting, Granada, Spain, 25-27 September 2019International audienceFlash floods frequently hit Southern France and cause heavy damages and fatalities. To enhance persons and goods safety, official flood forecasting services in France need accurate information and efficient models to optimize their decisions and policy in crisis management. Their forecasting is a serious challenge as heavy rainfalls that cause such floods are very heterogeneous in time and space. Such phenomena are typically nonlinear and more complex than classical flood events. This analysis had led to consider complementary alternatives to enhance the management of such situations. For decades, artificial neural networks have been proved very efficient to model nonlinear phenomena, particularly rainfall-discharge relations in various types of basins. They are applied in this study with two main goals: first, modeling flash floods on the Gardon de Mialet basin (Southern France); second, extract internal information from the model by using the KnoX: knowledge extraction method to provide new ways to improve models. The first analysis shows that the kind of nonlinear predictor strongly influences the representation of information, e.g., the main influent variable (rainfall) is more important in the recurrent and static models than in the feed-forward one. For understanding “long-term” flash floods genesis, recurrent and static models appear thus as better candidates, despite their lower performance. Besides, the distribution of weights linking the exogenous variables to the first layer of neurons is consistent with the physical considerations about spatial distribution of rainfall and response time of the hydrological system

Neural Networks for Karst Spring Management. Case of the Lez Spring (Southern France)

International audienc

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)

International audienc

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

Operational Turbidity Forecast Using Both Recurrent and Feed-Forward Based Multilayer Perceptrons

International Work-Conference on Time Series (ITISE), Granada, SPAIN, JUN 27-29, 2016International audienceApproximately 25% of the world population drinking water depends on karst aquifers. Nevertheless, due to their poor filtration properties, karst aquifers are very sensitive to pollutant transport and specifically to turbidity. As physical processes involved in solid transport (advection, diffusion, deposit.) are complicated and badly known in underground conditions, a black-box modelling approach using neural networks is promising. Despite the well-known ability of universal approximation of multilayer perceptron, it appears difficult to efficiently take into account hydrological conditions of the basin. Indeed these conditions depend both on the initial state of the basin (schematically wet or dry), and on the intensity of rainfalls. To this end, an original architecture has been proposed in previous works to take into account phenomenon at large temporal scale (moisture state), coupled with small temporal scale variations (rainfall). This architecture, called hereafter as ``two-branches'' multilayer perceptron is compared with the classical two layers perceptron for both kinds of modelling: recurrent and non-recurrent. Applied in this way to the Yport pumping well (Normandie, France) with 12 h lag time, it appears that both models proved crucial information: amplitude and synchronization are better with ``two-branches'' feed forward model when thresholds surpassing prediction is better using classical feed forward perceptron

Changes in flood risk and perception in catchments and cities

Flash floods are often responsible for many deaths and involve many material damages. Regarding Mediterranean karst aquifers, the complexity of connections, between surface and groundwater, as well as weather non-stationarity patterns, increase difficulties in understanding the basins behaviour and thus warning and protecting people. Furthermore, given the recent changes in land use and extreme rainfall events, knowledge of the past floods is no longer sufficient to manage flood risks. Therefore the worst realistic flood that could occur should be considered. Physical and processes-based hydrological models are considered among the best ways to forecast floods under diverse conditions. However, they rarely match with the stakeholders' needs. In fact, the forecasting services, the municipalities, and the civil security have difficulties in running and interpreting data-consuming models in real-time, above all if data are uncertain or non-existent. To face these social and technical difficulties and help stakeholders, this study develops two operational tools derived from these models. These tools aim at planning real-time decisions given little, changing, and uncertain information available, which are: (i) a hydrological graphical tool (abacus) to estimate flood peak discharge from the karst past state and the forecasted but uncertain intense rainfall; (ii) a GIS-based method (MARE) to estimate the potential flooded pathways and areas, accounting for runoff and karst contributions and considering land use changes. Then, outputs of these tools are confronted to past and recent floods and municipalities observations, and the impacts of uncertainties and changes on planning decisions are discussed. The use of these tools on the recent 2014 events demonstrated their reliability and interest for stakeholders. This study was realized on French Mediterranean basins, in close collaboration with the Flood Forecasting Services (SPC Med-Ouest, SCHAPI, municipalities)

Neural Networks Model as Transparent Box: Toward Extraction of Proxies to Better Assess Karst/River Interactions (Coulazou Catchment, South of France)

International audienceKarst catchments frequently exhibit complex exchanges between surface and subterranean flow. While the swing between surface flood and underground flood is complex, the ability to predict such behavior would be of great interest for flood forecasting and water recharge assessment. To this end an innovative methodology is proposed to visualize internal variables of a neural network model. It proves to be efficient to extract internal variables highly correlated to measured signals previously identified as proxy of the karst-river exchanges. The study focuses on a small Mediterranean catchment where karst/river interactions control the dynamic and genesis of surface floods. But the methodology is generic and can be applied to any catchment provided the availability of a sufficient database