Authors:
Ishmael S. Msiza
and
Tshilidzi Marwala
Affiliation:
University of Johannesburg, South Africa
Keyword(s):
Neural Network, Autoencoder Network, Multi-layer Perceptron, Water Demand, Time Series, Regression Approximation, Predictive Modelling, Hidden Units, Network Dimensionality, Arbitrary Complexity.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Formal Methods
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Neural Nets and Fuzzy Systems
;
Simulation and Modeling
;
Symbolic Systems
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 predic
tion 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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