ANNs approach to identify water demand drivers for Saf-Saf river basin

Sakaa, Bachir; Chaffai, Hicham; Hani, Azzedine

doi:10.1080/23249676.2020.1719220

Cited by 5 publications

(1 citation statement)

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“…Additionally, "soft computing", such as artificial neural networks (ANNs) and fuzzy logic (FL), tend to be more efficient and less time consuming in the modelling of complex systems [PAHLAVAN et al 2012;SONMEZ et al 2018]. Many reports discuss the efficiency of ANNs as a data driven technique to model and to forecast water consumption [AL-ZAHRANI, ABO-MONASAR 2015;BENNETT et al 2012;GHIASSI et al 2017;RANGEL et al 2017;SAKAA, HANI 2020]. A lot of work has been done in the subject.…”

Section: Introductionmentioning

confidence: 99%

Journal of Water and Land Development

Zeroual,

Hani,

Boustila

2021

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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: principal 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 characteristics 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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