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Abstract

The growing demand for fresh water and its scarcity are the major problems encountered in semi-arid cities. Two different techniques have been used to assess the main determinants of domestic water in the Sedrata City, North-East Algeria: prin-cipal component analysis (PCA) and artificial neural networks (ANNs). To create the ANNs models based on the PCA, twelve explanatory variables are initially investigated, of which nine are socio-economic parameters and three physical char-acteristics of building units. Two optimum ANNs models have been selected where correlation coefficients equal to 0.99 in training, testing and validation phases. In addition, results demonstrate that the combination of socio-economic parameters with physical characteristics of building units enhances the assessment of household water consumption.
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Authors and Affiliations

Menal Zeroual
1
Azzedine Hani
1
Amir Boustila
2

  1. University of Badji Mokhtar, Faculty of Earth Sciences, Laboratory of water resource and sustainable development, BP 12 / 23000 Annaba, Algeria
  2. University of Badji Mokhtar, Faculty of Earth Sciences, Laboratory of natural resource and development, Annaba, Algeria

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