The association between the monthly total ozone concentration and monthly maximum temperature over Kolkata (22.56°N, 88.30° E), India, has been explored in this paper. For this, the predictability of monthly maximum temperature based on the total ozone as predictor is investigated using Artificial Neural Network. The presence of persistence and similar cyclic patterns are revealed through autocorrelation and cross-correlation coefficients. Common cycles of length 12 and 6 have been identified through periodogram. Hence, a predictive model has been generated by Artificial Neural Network in the form of Multi Layer Perceptron (MLP) using scaled conjugate gradient learning with sigmoid non-linearity. After training and testing the network, an MLP with total ozone of month n as predictor and maximum temperature of month (n+. 1) as the target output is found as the best model. Performance of the model has been judged statistically. Finally, the MLP model has been compared with linear and non-linear regressions and the efficiency of MLP has been established over the regression models. © 2011.

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G CHATTOPADHYAY

B BANDYOPADHYAY

S PAUL

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Comptes Rendus - Geoscience

An information theoretic approach based on Shannon entropy is adopted in this study to discern the influence of pre-monsoon thunderstorm on some surface parameters. A few parameters associated with pre-monsoon thunderstorms over a part of east and northeast India are considered. Maximization of Shannon entropy is employed to test the relative contributions of these parameters in creating this weather phenomenon. It follows as a consequence of this information theoretic approach that surface temperature is the most important parameter among those considered. Finally, artificial neural network in the form of multilayer perceptron with backpropagation learning is attempted to develop predictive model for surface temperature. © 2018, Springer Science+Business Media B.V., part of Springer Nature.

Natural Hazards

Shannon entropy maximization supplemented by neurocomputing to study the consequences of a severe weather phenomenon on some surface parameters

The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots. © 2011 Springer-Verlag.

Theoretical and Applied Climatology

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

The present paper has adopted an autoregressive approach to inspect the time series of monthly maximum temperature (Tmax) over northeast India. Through autocorrelation analysis the Tmax time series of northeast India is identified as non-stationary, with a seasonality of 12 months, and it is also found to show an increasing trend by using both parametric and non-parametric methods. The autoregressive models of the reduced Tmax time series, which has become stationary on removal of the seasonal and the trend components from the original time series, were generated through Yule-Walker equations. The sixth order autoregressive model (AR(6)) is identified as a suitable representative of the Tmax time series based on the Akaike information criteria, and the prediction potential of AR(6) is also established statistically through Willmott's indices. Subsequently, autoregressive neural network models were generated as a multilayer perceptron, a generalized feed forward neural network and a modular neural network. An autoregressive neural network model of order four (AR-NN(4)), in the form of a modular neural network (MNN), has performed comparably well with that of AR(6) based on the high values of Willmott's indices and the low values of the prediction error. Therefore, AR-NN(4)-MNN will be a better option than AR(6) to forecast a time series, i.e. the monthly Tmax time series of northeast India, because AR-NN(4)-MNN requires fewer predictors for a superior forecast of a time series. © 2010 Royal Meteorological Society.

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Meteorological Applications

Univariate modelling of monthly maximum temperature time series over northeast India: Neural network versus Yule-Walker equation based approach

The present article reports studies to develop a univariate model to forecast the summer monsoon (June-August) rainfall over India. Based on the data pertaining to the period 1871-1999, the trend and stationarity within the time series have been investigated. After revealing the randomness and non-stationarity within the time series, the autoregressive integrated moving average (ARIMA) models have been attempted and the ARIMA(0,1,1) has been identified as a suitable representative model. Consequently, an autoregressive neural network (ARNN) model has been attempted and the neural network has been trained as a multilayer perceptron with the extensive variable selection procedure. Sigmoid non-linearity has been used while training the network. Finally, a three-three-one architecture of the ARNN model has been obtained and after thorough statistical analysis the supremacy of ARNN has been established over ARIMA(0,1,1). The usefulness of ARIMA(0,1,1) has also been described. © 2009 Académie des sciences.

Univariate modelling of summer-monsoon rainfall time series: Comparison between ARIMA and ARNN

The present study endeavors to generate autoregressive neural network (AR-NN) models to forecast the monthly total ozone concentration over Kolkata (22°34′, 88°22′), India. The issues associated with the applicability of neural network to geophysical processes are discussed. The autocorrelation structure of the monthly total ozone time series is investigated, and stationarity of the time series is established through the periodogram. From various autoregressive moving average (ARMA) and autoregressive models fit to the time series, the autoregressive model of order 10 is identified as the best. Subsequently, 10 autoregressive neural network (AR-NN) models are generated; the 10th order autoregressive neural network model with extensive input variable selection performs the best among all the competitive models in forecasting the monthly total ozone concentration over the study zone. © 2009 Elsevier Ltd. All rights reserved.

Computers and Geosciences

Autoregressive forecast of monthly total ozone concentration: A neurocomputing approach

This paper explores the observation made by the Earth Probe Total Ozone Mapping Spectrometer (EP/TOMS) to analyse the predictability of daily total ozone concentration over Kolkata, India. Latitude, longitude, aerosol index, reflectivity, sulphur dioxide index and total ozone concentration of a given day have been used as independent variables to predict total ozone concentration of the next day. Artificial neural network in the forms of a multilayer perceptron, generalized feed forward neural network, a radial basis function network and a modular neural network have been trained to generate predictive models. Performances of the models in the test cases have been judged with the help of four statistical parameters. Finally the models have been compared with multiple linear regression and the potential of generalized feed forward neural network has been established over the other proposed models. © 2008 Royal Meteorological Society.

Predicting daily total ozone over Kolkata, India: Skill assessment of different neural network models

The endeavor of the present paper is to investigate the existence of chaotic behavior in the underlying dynamics of the total ozone concentration over Arosa, Switzerland (9.68°E, 46.78° N). For this purpose, the correlation dimension method is employed to the mean monthly total ozone concentration data collected over a period of 40 years (1932-1971) at the above location. Based on the observation of a low correlation dimension value of 1 for this data set, the study reports the existence of low-dimensional chaotic behavior in the ozone concentration dynamics. © Springer-Verlag 2007.

Journal	Data powered by TypesetComptes Rendus - Geoscience
Publisher	Data powered by TypesetELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER
ISSN	1631-0713