A nonlinear rainfall-runoff model using neural network technique: example in fractured porous media

• Published in: Mathematical and computer modelling 2003-05, Vol.37 (9), p.1047-1061
• Main Authors: Lallahem, S., Mania, J.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Artificial neural networks; Chalk media; Computer science; control theory; systems; Connectionism. Neural networks; Exact sciences and technology; Flows through porous media; Fluid dynamics; Fractured media; Fundamental areas of phenomenology (including applications); Groundwater flow; Karst aquifer; Mathematical analysis; Mathematics; Nonhomogeneous flows; Partial differential equations; Physics; Sciences and techniques of general use
• ISSN: 0895-7177; 1872-9479
• DOI: 10.1016/S0895-7177(03)00117-1

One of the more advanced approaches for simulating groundwater flow in karstic and fractured porous media is the combination of a linear and a nonlinear model. The paper presents an attempt to determine outflow influencing parameters in order to simulate aquifer outflow. Our approach in this study is to create a productive interaction system between expert, mathematical model, MERO, and artificial neural networks (ANNs). The proposed method is especially suitable for the problem of large-scale and long-term simulation. In the present project, the first objective is to determine aquifer outflow influencing parameters by the use of MERO model, which gave a good results in a fissured and chalky media, and then introduce these parameters in neural network (NN). To determine outflow influencing parameters, we propose to test the NN under fourth different external input scenarios. The second objective is to investigate the effect of temporal information by taking current and past data sets. The good found results reveal the merit of ANNs-MERO combination and specifically multilayer perceptron (MLP) models. This methodology provided that the network with lower, lag and number hidden layer, consistently produced better performance.