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- W2185290332 abstract "Groundwater is a real wealth which requires rational management, monitoring and control achievable by the various methods that can classify them according to their degree of water mineralization lasting quality. Indeed, to assess the quality of groundwater, the knowledge of a certain number of indicators, such as the Electrical Conductivity EC, Organic material OM and the amount of Fecal Coliforms FC is paramount. This work seeks to analyze the prediction indicators of quality of groundwater Souss-Massa Morocco. Initially, methods based on neural models MLP (Multi Layer Perceptron) are applied for the prediction of quality indicators of groundwater. The choice of the architecture of the artificial neural network ANN MLP type is determined by the use of different statistical tests of robustness, i.e. the AIC criterion (Akaike Information Criteria), the test RMSE (Root-Mean-Squarre error) and the criterion MAPE (Maximum Average Percentage Error). Levenberg Marquardt algorithms are used to determine the weights and biases existing between the different layers of neural network. In a second step, a comparative study was launched between the neural prediction model MLP type and conventional statistical models, including total multiple linear regression. The results showed that the performance of neural prediction model ANN - MLP is clearly superior than those established by the total multiple linear regression TMLR." @default.
- W2185290332 created "2016-06-24" @default.
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- W2185290332 date "2014-01-01" @default.
- W2185290332 modified "2023-09-27" @default.
- W2185290332 title "Elaboration of stochastic mathématical models for the prediction of parameters indicative of groundwater quality case of souss Massa-Morocco" @default.
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