NDLI: Evaluation of the ability of an artificial neural network model to simulate the input-output responses of a large karstic aquifer: the La Rochefoucauld aquifer (Charente, France)

Content Provider	Springer Nature Link
Author	Kurtulus, Bedri Razack, Moumtaz
Copyright Year	2006
Abstract	The ability of artificial neural networks (ANN) to model the rainfall-discharge relationships of karstic aquifers has been studied in the La Rochefoucauld karst system, south-west France, which supplies water to the city of Angoulême. A neural networks model was developed based on MLP (multi-layer perceptron) networks and the Levenberg-Marquardt optimization algorithm. Raw rainfall data were used without transformation into effective rainfall. This allowed for the elimination of certain non-verifiable simplifying assumptions and their subsequent introduction into the modeling procedure. The raw rainfall and discharge data were divided into three groups for the training, the validation and the prediction test of the ANN model. The training and validation phases led to a very satisfactory calibration of the ANN model. The attempt to predict discharges showed that the ANN model is able to simulate the karstic aquifer discharges. The shape of the simulated hydrographs was found to be similar to that of the actual hydrographs. These encouraging results make it possible to consider interesting and new prospects for the modeling of karstic aquifers, which are highly non-linear systems.L’aptitude des réseaux de neurones artificiels (RNA) à modéliser les relations pluie-débit des aquifères karstiques a été évaluée sur le karst de La Rochefoucauld (Sud-Ouest de la France), qui fournit l’eau potable à la capitale régionale Angoulême. Un modèle RNA a été développé à cet effet, basé sur les réseaux PMC (Perceptron Multicouche) et l’algorithme d’optimisation de Levenberg-Marquardt. Les données de pluie utilisées concernent la pluie brute, sans transformation en pluie efficace, ce qui permet de s’affranchir de certaines hypothèses simplificatrices non vérifiables. Les données de pluie brute et de débit ont été réparties en 3 groupes pour l’apprentissage, la validation et le test de prédiction du RNA. Les phases d’apprentissage et de validation ont permis d’aboutir à une calibration très satisfaisante du modèle RNA. La tentative de prédiction a montré que le RNA est apte à simuler les débits de l’aquifère karstique à partir de la pluie brute. La forme des hydrogrammes simulés est semblable à celle des hydrogrammes réels. Les résultats obtenus sont très encourageants et permettent d’envisager des perspectives intéressantes et nouvelles de modélisation des aquifères karstiques, qui sont des systèmes hautement non-linéaires.Se ha estudiado la capacidad de las redes artificiales neurales (ANN) para modelizar las relaciones de lluvia-descarga de acuíferos kársticos en el sistema kárstico La Rochefocauld, al suroeste de Francia, el cual abastece de agua a la ciudad de Angoulême. Se desarrolló un modelo de redes neurales en base a redes MLP (Perceptron Multi-Capas) y el algoritmo de optimización Levenberg-Marquardt. Se utilizaron datos de lluvia sin la transformación hacia lluvia efectiva. Esto permitió la eliminación de ciertos supuestos simplificadores no verificables y su subsiguiente introducción en el procedimiento de modelizado. Los datos brutos de descarga y lluvia se dividieron en 3 grupos para la preparación, validación y la prueba de predicción del modelo ANN. Las fases de preparación y validación llevaron a una calibración muy satisfactoria del modelo ANN. El intento por predecir descargas mostró que el modelo ANN es capaz de simular las descargas del acuífero kárstico. Se encontró que la forma de los hidrogramas sintéticos es similar a la de los hidrogramas reales. Estos resultados alentadores hacen posible considerar prospectos nuevos e interesantes para el modelizado de acuíferos kársticos los cuales son sistemas altamente no-lineares.
Starting Page	241
Ending Page	254
Page Count	14
File Format	PDF
ISSN	14312174
Journal	Hydrogeology Journal
Volume Number	15
Issue Number	2
e-ISSN	14350157
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-09-23
Publisher Institution	International Association of Hydrogeologists
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural networks Karst Numerical modeling Non-linear system La Rochefoucauld aquifer Waste Water Technology Water Pollution Control Water Management Aquatic Pollution Geology Hydrogeology
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Water Science and Technology

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