Identification of the best architecture of a multilayer perceptron in modeling daily total ozone concentration over Kolkata,India |
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| Authors: | Syam S De Barin K De Goutami Chattopadhyay Suman Paul Dilip K Haldar Dipak K Chakrabarty |
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| Institution: | (1) Centro de Estudios Ambientales del Mediterraneo, Paterna, Valencia, Spain;(2) Fundacion CEAM, Parque Tecnologico Paterna, C. Charles R. Darwin, 14, E-46980 Paterna, Valencia, Spain |
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| Abstract: | Autoregressive neural network (AR-NN) models of various orders have been generated in this work for the daily total ozone
(TO) time series over Kolkata (22.56°N, 88.5°E). Artificial neural network in the form of multilayer perceptron (MLP) is implemented
in order to generate the AR-NN models of orders varying from 1 to 13. An extensive variable selection method through multiple
linear regression (MLR) is implemented while developing the AR-NNs. The MLPs are characterized by sigmoid non-linearity. The
optimum size of the hidden layer is identified in each model and prediction are produced by validating it over the test cases
using the coefficient of determination (R
2) and Willmott’s index (WI). It is observed that AR-NN model of order 7 having 6 nodes in the hidden layer has maximum prediction
capacity. It is further observed that any increase in the orders of AR-NN leads to less accurate prediction. |
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