Syam Sundar De

Bio: Syam Sundar De is an academic researcher from University of Calcutta. The author has contributed to research in topic(s): Ionosphere & Radio atmospheric. The author has an hindex of 7, co-authored 48 publication(s) receiving 126 citation(s).

Studies on the shift in the frequency of the first Schumann resonance mode during a solar proton event

Abstract: The variation of the first Schumann resonance (SR) frequency spectra observed from the recorded data over Kolkata (22.56°N, 88.5°E) during a solar proton event (SPE) on July 14, 2000 has been presented. It shows increase in frequency during X-ray bursts and decrease during the period of occurrence of an SPE. The results from some other locations for the same event are also reported. The severe X-ray bursts recorded just before the proton event exhibit enhancement in frequency of the first mode due to enhancement of ionization in the D-region of the ionosphere. Some attempts are made to explain the observed variation during solar proton events in terms of the perturbations within the Earth–ionosphere waveguide on the basis of two-layer-model.

Effects of a Solar Eclipse on the Propagation of VLF-LF Signals: Observations and Results

Abstract: The results from the measurements of some of the fundamental parameters (amplitude of sferics and transmitted signal, conductivity of lower ionosphere) of the ionospheric responses to the 22 July 2009 solar eclipse (partial: 91.7%) are shown. This study summarizes our results from sferics signals at 81 kHz and subionospheric transmitted signals at 19.8 and 40 kHz recorded at Agartala, Tripura (latitude: 23°N, longitude: 91.4°E). We observed significant absorption in amplitude of these signals during the eclipse period compared to their ambient values for the same period during the adjacent 7 days. The signal strength along their propagation paths was controlled by the eclipse associated decrease in ionization in the D-region of the ionosphere. Waveguide mode theory calculations show that the elevation of the height of lower ionosphere boundary of the Earth-ionosphere waveguide to a value where the conductivity parameter was 10 6 unit. The absorption in 81 kHz sferics amplitude is high compared to the absorption in the amplitude of 40 kHz signal transmitted from Japan. The simultaneous changes in the amplitudes of sferics and in the amplitude of transmitted signals assert some sort of coupling between the upper atmosphere and the Earth’s near-surface atmosphere prevailing clouds during solar eclipse.

The Effects of Solar Eclipse of August 1, 2008 on Earth’s Atmospheric Parameters

Abstract: Several experiments were undertaken at Kolkata (latitude: 22°34′N, longitude: 88°30′E) on the solar eclipse day of August 1, 2008 to observe the effects of the solar eclipse on Fair Weather Field (FWF) and VLF amplitude and phase. The experimental results presented here show significant deviations of the observed parameters from their normal values, as they are determined by the average of the records obtained on 5 days adjacent to the day of the solar eclipse.

Effects on atmospherics at 6 kHz and 9 kHz recorded at Tripura during the India-Pakistan Border earthquake

Abstract: . The outcome of the results of some analyses of electromagnetic emissions recorded by VLF receivers at 6 kHz and 9 kHz over Agartala, Tripura, the North-Eastern state of India (Lat. 23° N, Long. 91.4° E) during the large earthquake at Muzaffarabad (Lat. 34.53° N, Long. 73.58° E) at Kashmir under Pakistan have been presented here. Spiky variations in integrated field intensity of atmospherics (IFIA) at 6 and 9 kHz have been observed 10 days prior (from midnight of 28 September 2005) to the day of occurrence of the earthquake on 8 October 2005 and the effect continued, decayed gradually and eventually ceased on 16 October 2005. The spikes distinctly superimposed on the ambient level with mutual separation of 2–5 min. Occurrence number of spikes per hour and total duration of their occurrence have been found remarkably high on the day of occurrence of the earthquake. The spike heights are higher at 6 kHz than at 9 kHz. The results have been explained on the basis of generation of electromagnetic radiation associated with fracture of rocks, their subsequent penetration into the Earth's atmosphere and finally their propagation between Earth-ionosphere waveguide. The present observation shows that VLF anomaly is well-confined between 6 and 9 kHz.

Analyses of Schumann resonance spectra from Kolkata and their possible interpretations

Abstract: The paper deals with the study of Schumann resonance data set recorded at Kolkata (Latitude 22.56oN). The results of analyses are confined to a period of one year (January to December 2000). The amplitude and frequency fluctuations along with some aspects of Schumann resonances (SR) during the period are investigated. The variation of global thunderstorm activity as inferred from monthly intensity fluctuations of global SR signals over Kolkata and Modra (Latitude 48.61oN) is presented and the observed difference has been interpreted.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling

Abstract: Summary Artificial neural networks (ANNs) becomes very popular tool in hydrology, especially in rainfall–runoff modelling. However, a number of issues should be addressed to apply this technique to a particular problem in an efficient way, including selection of network type, its architecture, proper optimization algorithm and a method to deal with overfitting of the data. The present paper addresses the last, rarely considered issue, namely comparison of methods to prevent multi-layer perceptron neural networks from overfitting of the training data in the case of daily catchment runoff modelling. Among a number of methods to avoid overfitting the early stopping, the noise injection and the weight decay have been known for about two decades, however only the first one is frequently applied in practice. Recently a new methodology called optimized approximation algorithm has been proposed in the literature. Overfitting of the training data leads to deterioration of generalization properties of the model and results in its untrustworthy performance when applied to novel measurements. Hence the purpose of the methods to avoid overfitting is somehow contradictory to the goal of optimization algorithms, which aims at finding the best possible solution in parameter space according to pre-defined objective function and available data. Moreover, different optimization algorithms may perform better for simpler or larger ANN architectures. This suggest the importance of proper coupling of different optimization algorithms, ANN architectures and methods to avoid overfitting of real-world data – an issue that is also studied in details in the present paper. The study is performed for Annapolis River catchment, characterized by significant seasonal changes in runoff, rapid floods during winter and spring, moderately dry summers, severe winters with snowfall, snow melting, frequent freeze and thaw, and presence of river ice. The present paper shows that the elaborated noise injection method may prevent overfitting slightly better than the most popular early stopping approach. However, the implementation of noise injection to real-world problems is difficult and the final model performance depends significantly on a number of very technical details, what somehow limits its practical applicability. It is shown that optimized approximation algorithm does not improve the results obtained by older methods, possibly due to over-simplified criterion of stopping the algorithm. Extensive calculations reveal that Evolutionary Computation-based algorithm performs better for simpler ANN architectures, whereas classical gradient-based Levenberg–Marquardt algorithm is able to benefit from additional input variables, representing precipitation and snow cover from one more previous day, and from more complicated ANN architectures. This confirms that the curse of dimensionality has severe impact on the performance of Evolutionary Computing methods.

Statistical model for standard seismicity and detection of anomalies by residual analysis

Abstract: A statistical point-process model is derived to describe the standard activity of earthquake occurrences by assuming that general seismicity is given by the superposition of aftershock sequences. The parameters are estimated ty the maximum likelihood method. Using the estimated model, the “residual point process” of the data is defined and used to find the anomalies which are included in the data set but not captured in the considered model for the standard seismicity. For instance, seismic quiescences can be measured quantitatively by using the residual process. Some examples are provided to illustrate such analyses. Furthermore, a time series of the magnitudes on the residual point process is considered, to investigate its dependence either on the time or on the history of the seismicity. By assuming the exponential distribution at each time and modelling of the b- value , we can examine such dependences and estimate them. Two practical examples are shown.

Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach

Abstract: Summary Although neural networks have been widely applied to various hydrological problems, including river flow forecasting, for at least 15 years, they have usually been trained by means of gradient-based algorithms. Recently nature inspired Evolutionary Computation algorithms have rapidly developed as optimization methods able to cope not only with non-differentiable functions but also with a great number of local minima. Some of proposed Evolutionary Computation algorithms have been tested for neural networks training, but publications which compare their performance with gradient-based training methods are rare and present contradictory conclusions. The main goal of the present study is to verify the applicability of a number of recently developed Evolutionary Computation optimization methods, mostly from the Differential Evolution family, to multi-layer perceptron neural networks training for daily rainfall–runoff forecasting. In the present paper eight Evolutionary Computation methods, namely the first version of Differential Evolution (DE), Distributed DE with Explorative–Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization and Efficient Population Utilization Strategy Particle Swarm Optimization are tested against the Levenberg–Marquardt algorithm – probably the most efficient in terms of speed and success rate among gradient-based methods. The Annapolis River catchment was selected as the area of this study due to its specific climatic conditions, characterized by significant seasonal changes in runoff, rapid floods, dry summers, severe winters with snowfall, snow melting, frequent freeze and thaw, and presence of river ice – conditions which make flow forecasting more troublesome. The overall performance of the Levenberg–Marquardt algorithm and the DE with Global and Local Neighbors method for neural networks training turns out to be superior to other Evolutionary Computation-based algorithms. The Levenberg–Marquardt optimization must be considered as the most efficient one due to its speed. Its drawback due to possible sticking in poor local optimum can be overcome by applying a multi-start approach.

ELF Electromagnetic Waves from Lightning: The Schumann Resonances

Abstract: Lightning produces electromagnetic fields and waves in all frequency ranges. In the extremely low frequency (ELF) range below 100 Hz, the global Schumann Resonances (SR) are excited at frequencies of 8 Hz, 14 Hz, 20 Hz, etc. This review is aimed at the reader generally unfamiliar with the Schumann Resonances. First some historical context to SR research is given, followed by some theoretical background and examples of the extensive use of Schumann resonances in a variety of lightning-related studies in recent years, ranging from estimates of the spatial and temporal variations in global lighting activity, connections to global climate change, transient luminous events and extraterrestrial lightning. Both theoretical and experimental results of the global resonance phenomenon are presented. It is our hope that this review will increase the interest in SR among researchers previously unfamiliar with this phenomenon.