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

Abstract

This paper presents artificial neural network (ANN) techniques such as generalized regression neural networks (GRNNs), radial basis neural networks (RBFNNs) and multilayer perceptron neural networks (MLPNNs) for predicting quarterly water demand (QWD). The data set including total of 720 data records is divided into two subsets, training and testing. Various ANN models depending on the combination of antecedent values of water demand, temperature, rainfall and population are constructed and the best-fit input structure is examined. The performance of ANN models in training and testing phases are compared with the observed water demand values to select the best-fit forecasting model. For this purpose, some performance criteria such as root mean square error, coefficient of determination (R²) and accuracy factor (A_f) are evaluated for different models (GRNN, RBFNN and MLPNN). The results indicated that MLPNN outperforms all other ANN techniques (GRNN and RBFNN) in the forecasting of QWD.

Abbreviations: A_f, accuracy factor; ANN, artificial neural networks; GRNN, general regression neural network; MLPNN, multilayer perceptron neural network; P, seasonal mean rainfall (in mm); Pop, population (in number); RBFNN, radial basis function neural network; RMSE, Root mean square error; R², Coefficient of determination; T, seasonal mean temperature (in °C); QWD: quarterly water demand (measured by 10⁶m³)

KEYWORDS:

• Quarterly water demand
• GRNN model
• RBFNN model
• MLPNN model
• Saf-Saf river basin municipalities

Additional information

Notes on contributors

Bachir Sakaa

Bachir Sakaa earned a PhD degree in June of 2013 in Water Resources Management from Annaba University. He started working at the Scientific and Technical Research Centre on Arid Areas C.R.S.T.R.A in May of 2011. Currently, Bachir is working at the Water Resources and Land Degradation Department. His areas of interest include hydrogeology, water quality, hydrology and modeling and machine learning.

Hicham Chaffai

Hicham Chaffai is a Full Professor at Annaba University in the Department of Geology. He conducts research to solve complex problems associated with environmental pollution. Her main research interests include Hydro geological and hydrological modeling, water quality and pollution.

Azzedine Hani

Azzedine Hani is a Full Professor at Annaba University in the Department of Geology. Mr Azzedine has over 19 years of hydrogeology and water management experience. His areas of interest include water resources management, water pollution, environmental assessment and modeling.