Jacky Mania

Rainfall-runoff model usingan artificial neural network approach

The use of artificial neural networks (ANNs) is becoming increasingly common in the analysis of hydrology and water resources problems. In this research, an ANN was developed and used to model the rainfall-runoff relationship, in a catchment located in a semiarid climate in Morocco. The multilayer perceptron (MLP) neural network was chosen for use in the current study. The results and comparative study indicate that the artificial neural network method is more suitable to predict river runoff than classical regression model.

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

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.

Investigation of a chalk aquifer: advantages and limits of diffusion samplers and low-flow sampling techniques

Abstract In order to estimate the vertical contaminant distributions in a chalk aquifer, two sampling techniques are used in the piezometers. The first is passive multi-level sampling, using dialysis membrane diffusion samplers. The second is the low-flow purging technique. Geochemical groundwater zones are also established by profiles of temperature, conductivity, dissolved oxygen, pH and redox potential. These sampling techniques enhance three-dimensional visualization of the chemical and physical parameters in the chalk aquifer. Except for the cases where there are large variations in the concentrations, these two methods provide approximately the same results. But these techniques should not be applied with the same aim. Dialysis membrane diffusion samplers preserve the weak chemical variations of an aquifer, but lead to mistakes in determining iron concentrations. Conversely, the low-flow rate technique preserves the iron concentration, but the depth resolution is weaker (on a scale much greater than 1 m) because pumping produces a mix of flow horizons.