Performance Analysis of Artificial Neural Networks Training Algorithms and Transfer Functions for Medium-Term Water Consumption Forecasting
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 4
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 model’s 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 model’s 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.
Authors and Affiliations
Lemuel Clark P. Velasco, Angelie Rose B. Granados, Jilly Mae A. Ortega, Kyla Veronica D. Pagtalunan
Keywords
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- EP ID EP285904
- DOI 10.14569/IJACSA.2018.090419
- Views 88
- Downloads 0
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