Backpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology Data
Soo See Chai, Wei Keat Wong and Kok Luong Goh
DOI : 10.3844/jcssp.2016.191.200
Journal of Computer Science
Volume 12, Issue 4
Rainfall is one of the important weather variables that vary in space and time. High mean daily rainfall (>30 mm) has a high possibility of resulting in flood. Accurate prediction of this variable would save human lives and properties. Soft computing methods have been widely applied in this field. Among the various soft computing methods, Artificial Neural Network (ANN) is the most commonly used methodology. While numerous ANN algorithms were applied, the most commonly applied are the Backpropagation (BPN) and Radial Basis Function (RFN) models. However, there was no research conducted to verify which model among these two produces a superior result. Therefore, this study will fill this gap. In this study, using the meteorology data, the two ANN models were trained to classify the rainfall intensity based on four different classes: Light (<10 mm), moderate (11-30 mm), heavy (31-50 mm) and very heavy (>51 mm). The architecture of the neural networks models based on the different combination of inputs and number of hidden neurons to obtain the optimum classification were verified in this study. The influence of the number of training data on the classification results was also analyzed. Results obtained showed, in term of classification accuracy, BPN model performed better than the RFN model. However, in term of consistency, the RFN model outperformed BPN model.
© 2016 Soo See Chai, Wei Keat Wong and Kok Luong Goh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.