Performance Analysis of Artificial Neural Networks Training Algorithms and Transfer Functions for Medium-Term Water Consumption Forecasting

Journal

Title	Performance Analysis of Artificial Neural Networks Training Algorithms and Transfer Functions for Medium-Term Water Consumption Forecasting Posted by Lemuel Clark Velasco
Authors	Velasco, Lemuel Clark ; Granados, Angelie Rose ; Ortega, Jilly Mae, Pagtalunan, Kyla Veronica
Publication date	2018/5
Journal	International Journal of Advanced Computer Science and Applications IJACSA
Volume	9
Issue	4
Pages	8
Publisher	The Science and Information Organization
Abstract	Artificial Neural Network (ANN) is a widely used machine learning pattern recognition technique in predicting water resources based on historical data. ANN has the ability to forecast close to accurate prediction given the appropriate training algorithm and transfer function along with the modelâs learning rate and momentum. In this study, using the Neuroph Studio platform, six models having different combination of training algorithms namely Backpropagation, Backpropagation with Momentum and Resilient Propagation and transfer functions namely Sigmoid and Gaussian were compared. After determining the ANN modelâs input, hidden and output neurons from its respective layers, this study compared data normalization techniques and showed that Min-Max normalization yielded better results in terms of Mean Square Error (MSE) compared to Max normalization. Out of the six models tested, Model 1 which was composed of Backpropagation training algorithm and Sigmoid transfer function yielded the lowest MSE. Moreover, learning rate and momentum value for the models of 0.2 and 0.9 respectively resulted to very minimal error in terms of MSE. The results obtained in this research clearly suggest that ANN can be a viable forecasting technique for medium-term water consumption forecasting.
Index terms / Keywords	Artificial neural network; backpropagation; water consumption forecasting
DOI	10.14569/IJACSA.2018.090419
URL	http://thesai.org/Publications/ViewPaper?Volume=9&Issue=4&Code=IJACSA&SerialNo=19