A practical guide to neural nets
A practical guide to neural nets
Neural network structures: form follows function
Handbook of neural computer applications
Hybrid Neural Network and Expert Systems
Hybrid Neural Network and Expert Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Mathematical and Computer Modelling: An International Journal
Monthly rainfall estimation using data-mining process
Applied Computational Intelligence and Soft Computing
| Hi-index | 0.98 |
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.