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Identification of the best architecture of a multilayer perceptron in modeling daily total ozone concentration over Kolkata, India

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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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Authors and Affiliations

  1. Centre of Advanced Study in Radio Physics and Electronics, University of Calcutta, Kolkata, India

    Syam S. De, Goutami Chattopadhyay, Suman Paul & Dilip K. Haldar

  2. Department of Physics, Tripura University, Tripura (West), India

    Barin K. De

  3. Centre for Environment Survey, Vidyasagar Society, Ahmedabad, India

    Dipak K. Chakrabarty

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  1. Syam S. De
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De, S.S., De, B.K., Chattopadhyay, G. et al. Identification of the best architecture of a multilayer perceptron in modeling daily total ozone concentration over Kolkata, India. Acta Geophys. 59, 361–376 (2011). https://doi.org/10.2478/s11600-010-0047-0

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