NDLI: Simultaneous parameter identification of a heterogeneous aquifer system using artificial neural networks

Content Provider	Springer Nature Link
Author	Karahan, Halil Ayvaz, M. Tamer
Copyright Year	2008
Abstract	An artificial neural network (ANN) model is proposed for the simultaneous determination of transmissivity and storativity distributions of a heterogeneous aquifer system. ANNs may be useful tools for parameter identification problems due to their ability to solve complex nonlinear problems. As an extension of previous study—Karahan H, Ayvaz MT (2006) Forecasting aquifer parameters using artificial neural networks, J Porous Media 9(5):429–444—the performance of the proposed ANN model is tested on a two-dimensional hypothetical aquifer system for transient flow conditions. In the proposed ANN model, Cartesian coordinates of observation wells, associated piezometric heads and observation time are used as inputs while corresponding transmissivity and storativity values are used as outputs. The training, validation and testing processes of the ANN model are performed under two scenarios. In scenario 1, all the sampled data are used through the simulation time. However, in the scenario 2, there are data gaps due to irregular observations. By using the determined synaptic network weights, transmissivity and storativity distributions are predicted. In addition, the performance of the proposed ANN is tested for different noise data conditions. Results showed that the developed ANN model may be used in simultaneous aquifer parameter estimation problems.Un modèle de réseau neuronal artificel (ANN) est proposé pour la détermination simultanée des distributions des transmissivités et coefficients d’emmagasinement dans un système aquifère hétérogène. Les ANN peuvent constituer des outils utiles pour les problèmes d’identification de paramètres, grâce à leur capacité à résoudre des problèmes complexes non-linéaires. Dans la continuité de l’étude précédente—Karahan H, Ayvaz MT (2006) Forecasting aquifer parameters using artificial neural networks [Estimation des paramètres des aquifères par un réseau neuronal artificiel], J Porous Media 9(5):429–444)—les performances du modèle ANN proposé sont éprouvées sur un système aquifère bidimensionnel hypothétique en régime transitoire. Dans le modèle ANN proposé, les coordonnées cartésiennes des piézomètres, leurs niveaux piézométriques et les temps d’observation sont utilisés comme entrées, et les valeurs de transmissivité et coefficients d’emmagasinement correspondants comme sorties. Les procédures de mise en œuvre, de validation et de test du modèle ANN suivent deux scénarios différents. Dans le scénario 1, toutes les données acquises sont utilisées lors de la simulation, tandis que les données présentent des lacunes dues à l’irrégularité des observations dans le scénario 2. Les distributions des transmissivités et coefficients d’emmagasinement sont estimées à partir des pondérations du réseau synaptique. Les résultats ont démontré que le modèle ANN développé peut être utilisé dans le cas de problèmes simultanés d’estimation des paramètres des aquifères.Se propone un modelo de red neural artificial (RNA) para la determinación simultánea de las distribuciones del coeficiente de almacenamiento y de transmisividad de un sistema de acuífero heterogéneo. Las RNA pueden ser herramientas útiles en problemas de identificación de parámetros debido a su capacidad para resolver problemas complejos no lineales. Como parte de la ampliación de un estudio previo—Karahan H, Ayvaz MT (2006) Forecasting aquifer paramters using artificial neural networks [Predicción de parámetros de acuífero usando redes neurales artificiales], J Porous Media 9(5):429–444—se evalúa el desempeño del modelo propuesto RNA en un sistema acuífero hipotético de dos dimensiones en condiciones de flujo transitorio. En el modelo RNA propuesto se usan como entradas las coordenadas Cartesianas de pozos de observación, presiones piezométricas asociadas y tiempo de observación mientras que los valores correspondientes del coeficiente de almacenamiento y de transmisividad se usan como salidas. Se utilizan dos escenarios para los procesos de evaluación, validación y entrenamiento del modelo RNA. En el escenario 1 todos los datos muestreados se usan a través del tiempo de simulación. Sin embargo, en el escenario 2 existen brechas en los datos debido a observaciones irregulares. Mediante el uso de pesos de redes sinápticas determinados se predicen distribuciones de coeficiente de almacenamiento y de transmisividad. Además se evalúa el desempeño de la RNA propuesta para distintas condiciones de datos con ruido. Los resultados muestran que el modelo RNA puede ser usado en problemas de estimación simultánea de parámetros de acuíferos.
Starting Page	817
Ending Page	827
Page Count	11
File Format	PDF
ISSN	14312174
Journal	Hydrogeology Journal
Volume Number	16
Issue Number	5
e-ISSN	14350157
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-02-05
Publisher Institution	International Association of Hydrogeologists
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Parameter identification Inverse modeling Neural networks Multi-parameters Groundwater flow 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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