Keyword(s):Neural Network, Autoencoder Network, Multi-layer Perceptron, Water Demand, Time Series, Regression Approximation, Predictive Modelling, Hidden Units, Network Dimensionality, Arbitrary Complexity.

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Abstract: Following a number of studies that have interrogated the usability of an autoencoder neural network in various
classification and regression approximation problems, this manuscript focuses on its usability in water demand
predictive modelling, with the Gauteng Province of the Republic of South Africa being chosen as a case study.
Water demand predictive modelling is a regression approximation problem. This autoencoder network is
constructed from a simple multi-layer network, with a total of 6 parameters in both the input and output
units, and 5 nodes in the hidden unit. These 6 parameters include a figure that represents population size
and water demand values of 5 consecutive days. The water demand value of the fifth day is the variable of
interest, that is, the variable that is being predicted. The optimum number of nodes in the hidden unit is
determined through the use of a simple, less computationally expensive technique. The performance of this
network is measured against prediction accuracy, average prediction error, and the time it takes the network
to generate a single output. The dimensionality of the network is also taken into consideration. In order to
benchmark the performance of this autoencoder network, a conventional neural network is also implemented
and evaluated using the same measures of performance. The conventional network is slightly outperformed
by the autoencoder network.(More)

Following a number of studies that have interrogated the usability of an autoencoder neural network in various classification and regression approximation problems, this manuscript focuses on its usability in water demand predictive modelling, with the Gauteng Province of the Republic of South Africa being chosen as a case study. Water demand predictive modelling is a regression approximation problem. This autoencoder network is constructed from a simple multi-layer network, with a total of 6 parameters in both the input and output units, and 5 nodes in the hidden unit. These 6 parameters include a figure that represents population size and water demand values of 5 consecutive days. The water demand value of the fifth day is the variable of interest, that is, the variable that is being predicted. The optimum number of nodes in the hidden unit is determined through the use of a simple, less computationally expensive technique. The performance of this network is measured against prediction accuracy, average prediction error, and the time it takes the network to generate a single output. The dimensionality of the network is also taken into consideration. In order to benchmark the performance of this autoencoder network, a conventional neural network is also implemented and evaluated using the same measures of performance. The conventional network is slightly outperformed by the autoencoder network.

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Msiza, I.; Msiza, I.; Marwala, T. and Marwala, T. (2016). Autoencoder Networks for Water Demand Predictive Modelling.In Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-199-1, pages 231-238. DOI: 10.5220/0005977202310238

@conference{simultech16, author={Ishmael S. Msiza. and Ishmael S. Msiza. and Tshilidzi Marwala. and Tshilidzi Marwala.}, title={Autoencoder Networks for Water Demand Predictive Modelling}, booktitle={Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,}, year={2016}, pages={231-238}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0005977202310238}, isbn={978-989-758-199-1}, }

TY - CONF

JO - Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, TI - Autoencoder Networks for Water Demand Predictive Modelling SN - 978-989-758-199-1 AU - Msiza, I. AU - Msiza, I. AU - Marwala, T. AU - Marwala, T. PY - 2016 SP - 231 EP - 238 DO - 10.5220/0005977202310238