Goutami Chattopadhyay

Multivariate forecast of winter monsoon rainfall in India using SST anomaly as a predictor: Neurocomputing and statistical approaches

In this article, the complexities in the relationship between rainfall and sea surface temperature (SST) anomalies during the winter monsoon over India were evaluated statistically using scatter plot matrices and autocorrelation functions. Linear, as well as polynomial trend equations were obtained, and it was observed that the coefficient of determination for the linear trend was very low and it remained low even when polynomial trend of degree six was used. An exponential regression equation and an artificial neural network with extensive variable selection were generated to forecast the average winter monsoon rainfall of a given year using the rainfall amounts and the SST anomalies in the winter monsoon months of the previous year as predictors. The regression coefficients for the multiple exponential regression equation were generated using Levenberg-Marquardt algorithm. The artificial neural network was generated in the form of a multilayer perceptron with sigmoid non-linearity and genetic-algorithm based variable selection. Both of the predictive models were judged statistically using the Willmotts index, percentage error of prediction, and prediction yields. The statistical assessment revealed the potential of artificial neural network over exponential regression.

Mann–Kendall trend analysis of tropospheric ozone and its modeling using ARIMA

The present work reports studies on the spatial distribution of tropospheric ozone extending over both southern and northern hemispheres. This study is based on a univariate approach to the spatial data series obtained at regular spatial intervals. Mann–Kendalls (MK) trend analysis has been carried out to discern the trend within the spatial distribution of the tropospheric ozone, and it has been observed that in all seasons, except monsoon (JJAS), there is a linear trend within the spatial distribution. Studying both monthly and seasonal behavior through autoregressive integrated moving average (ARIMA), it has been revealed that ARIMA (0,2,2) can be used as a representative of the spatially distributed tropospheric ozone over southern and northern hemispheres. The representative model has been confirmed through the study of Willmotts index and prediction yield.

Modeling and Prediction of Monthly Total Ozone Concentrations by Use of an Artificial Neural Network Based on Principal Component Analysis

In the work discussed in this paper we considered total ozone time series over Kolkata (22°34′10.92″N, 88°22′10.92″E), an urban area in eastern India. Using cloud cover, average temperature, and rainfall as the predictors, we developed an artificial neural network, in the form of a multilayer perceptron with sigmoid non-linearity, for prediction of monthly total ozone concentrations from values of the predictors in previous months. We also estimated total ozone from values of the predictors in the same month. Before development of the neural network model we removed multicollinearity by means of principal component analysis. On the basis of the variables extracted by principal component analysis, we developed three artificial neural network models. By rigorous statistical assessment it was found that cloud cover and rainfall can act as good predictors for monthly total ozone when they are considered as the set of input variables for the neural network model constructed in the form of a multilayer perceptron. In general, the artificial neural network has good potential for predicting and estimating monthly total ozone on the basis of the meteorological predictors. It was further observed that during pre-monsoon and winter seasons, the proposed models perform better than during and after the monsoon.

Acceleration of the Universe in presence of tachyonic field

Abstract In this letter, we have assumed that the Universe is filled in tachyonic field with potential, which gives the acceleration of the Universe. For certain choice of potential, we have found the exact solutions of the field equations. We have shown the decaying nature of potential. From recently developed statefinder parameters, we have investigated the role of tachyonic field in different stages of the evolution of the Universe.

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

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.

Univariate approach to the monthly total ozone time series over Kolkata, India: autoregressive integrated moving average (ARIMA) and autoregressive neural network (AR-NN) models

This study reports univariate modelling methodologies applied to the monthly total ozone concentration (TOC) over Kolkata (22°32′, 88°20′), India, derived from the measurements made by the Earth Probe Total Ozone Mapping Spectrometer (EP/TOMS). The univariate models have been generated in two forms, namely autoregressive integrated moving average (ARIMA) and autoregressive neural network (AR-NN). Three ARIMA models in the forms of ARIMA(1,1,1), ARIMA(0,1,1) and ARIMA(0,2,2) and 11 autoregressive neural network models, AR-NN(n), have been generated for a time series. Goodness of fit of the models to the time series of monthly TOC has been assessed using prediction error, Pearson correlation coefficient and Willmotts indices. After rigorous skill assessment, the ARIMA (0,2,2) has been identified as the best predictive model for the time series under study.

Monthly sunspot number time series analysis and its modeling through autoregressive artificial neural network

This study reports a statistical analysis of monthly sunspot number time series and observes nonhomogeneity and asymmetry within it. Using the Mann-Kendall test a linear trend is revealed. After identifying stationarity within the time series we generate autoregressive AR(p) and autoregressive moving average (ARMA(p, q) . Based on the minimization of AIC we find 3 and 1 as the best values for p and q , respectively. In the next phase, autoregressive neural network (AR-NN(3)) is generated by training a generalized feedforward neural network (GFNN). Assessing the model performances by means of Willmott’s index of second order and the coefficient of determination, the performance of AR-NN(3) is identified to be better than AR(3) and ARMA(3,1).

The possible association between summer monsoon rainfall in India and sunspot numbers

Studies are presented on the association between mean annual sunspot numbers (SNs) and the summer monsoon rainfall over India. Sunspots are related directly to solar magnetism, which governs the source of the solar wind. SN is correlated with the 10.7 cm solar flux, a quantity measured by remote sensing techniques. The statistical properties of the time series for SN and summer monsoon rainfall were studied and it was found that, although the SNs exhibit persistence, this is not the case for the mean annual summer monsoon rainfall. The cross-correlations were also studied. After a Box–Cox transformation, a time spectral analysis was conducted and it was found that both of the time series have an important spectrum at the fifth harmonic. A neural network model was developed on the data series averaged continuously over 5 years and this neural network could be used to establish a predictor–predictand relationship between the SNs and the mean yearly summer monsoon rainfall over India.

A factor analysis and neural network-based validation of the Varotsos-Cracknell theory on the 11-year solar cycle

The validation of the recently proposed Guttenberg–Richter law in the 11‐year solar cycle has been attempted in this paper through the employment of an Artificial Neural Network (ANN) in the form of the Multilayer Perceptron (MLP). The 11‐year solar cycle has been reviewed using the autocorrelation function. Factor analysis has been attempted to identify the most important predictors for the ANN‐based prediction of yearly sunspot numbers. Solar cycle length has been predicted through ANN, and its predictive ability has been examined by comparing with regression approaches. After rigorous study ANN has been found to be more efficient than the regression approach in predicting the solar cycle length. Finally, the existence of the Guttenberg–Richter law in the solar cycle has been validated by ANN.

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

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.