Evaluation and forecasting of daily groundwater outflow in a small chalky watershed

The purpose of this research is to include expert knowledge as one part of the modelling system and therefore offer the chance to create a productive interaction system between expert, mathematical model (MMO8) and artificial neural networks (ANNs). In the present project, the first objective is to...

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Bibliographic Details
Published in:Hydrological processes 2003-06, Vol.17 (8), p.1561-1577
Main Authors: Lallahem, S., Mania, J.
Format: Article
Language:English
Subjects:
back propagation algorithm
chalk
Earth sciences
Earth, ocean, space
Exact sciences and technology
fractured rock
groundwater
Hydrogeology
Hydrology. Hydrogeology
neural networks
stopped training approach
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Summary:The purpose of this research is to include expert knowledge as one part of the modelling system and therefore offer the chance to create a productive interaction system between expert, mathematical model (MMO8) and artificial neural networks (ANNs). In the present project, the first objective is to determine some parameters by the MMO8 model, introduced as ANN input parameters to forecast spring outflow. The second objective is first to investigate the effect of temporal information by taking current and past data sets and then to forecast spring outflow. The good results obtained reveal the merit of the ANNs–MMO8 combination, and specifically multilayer perceptron (MLP) models. This methodology, for a network with lower, lag and number hidden layer, consistently produced better performance. Copyright © 2003 John Wiley & Sons, Ltd.
ISSN:0885-6087
1099-1085
DOI:10.1002/hyp.1199