Exploring of the summer monsoon rainfall around the Himalayas in time domain through maximization of Shannon entropy

Saha, Sanghati; Chattopadhyay, Surajit

doi:10.1007/s00704-020-03186-4

Cited by 7 publications

(5 citation statements)

References 46 publications

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“…In particular, several studies have been carried out in Australia and overseas to investigate the relationships between seasonal rainfall and climate phenomenon (Kirono and Kent 2011;Abbot and Marohasy 2012;Mekanik et al 2013;Hossain et al 2018a). Saha and Chattopadhyay (2020) carried out theory-based investigation pertaining the time series of rainfall in seasonal scale as well as yearly scale in the Himalayas during the summer monsoon. Pal et al (2020) carried out study to investigate the behaviour of the time series of rainfall during the summer in northeast India.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Khastagir

Hossain

Anwar

2022

Meteorol Atmos Phys

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Precipitation is one of the most intrinsic resources for manifold industrial activities all over Western Australia; consequently, immaculate rainfall prediction is indispensable for flood mitigation as well as water resources management. This study investigated the performance of artificial neural networks (ANN) and Linear multiple regression (LMR) analysis to forecast long-term seasonal spring rainfall in Western Australia, using lagged El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as potential climatic phenomena. The ANN was developed in the form of multilayer perceptron using Levenberg–Marquardt algorithm and subsequently LMR was used with statistical significance for future spring rainfall forecast. The total climatic dataset has been divided into calibration and testing phases to determine the efficacy of the developed models. Different statistical skill tests such as root mean square error (RMSE), mean absolute error (MAE), and Willmott index of agreement ‘d’ were used to assess the efficacy of LMR and ANN modelling. In general, LMR has lower MAE and RMSE values as compared to ANN for most of the stations during calibration and testing periods, whereas ANN models performed better than LMR models based on ‘d’ values. The overall statistical analysis paradigm suggests the efficacy of LMR over ANN models for rainfall forecasting using more climatic variables. As a result, the developed LMR model, incorporated with lagged global climate indices, will facilitate the adequate preparedness for the risks associated with potential droughts in the study region.

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Section: Introductionmentioning

confidence: 99%

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Khastagir

Hossain

Anwar

2022

Meteorol Atmos Phys

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“…This measure is through DST; before that, we generated fuzzy sets, and through alpha-cuts, we built focal elements required for constructing the joint belief function. In this context, let us compare the current procedure with another work where one of the present authors has quantified the uncertainty through Shannon entropy (Saha and Chattopadhyay, 2020) [45]. This work shows how entropy is affected by the fluctuation of mean rainfall on seasonal and yearly scales.…”

Section: Discussionmentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

“…Contrary to the study of [45], the current work has explored the possibility of DST to probe the intrinsic uncertainty associated with a climatological parameter. Although the basic procedure of the present study differs from the work [45], the flexibility of both approaches is noteworthy. Prediction of climatological time series through artificial neural networks, an essential component of soft computing techniques, was reported in [46].…”

Section: Discussionmentioning

confidence: 99%

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An Interpretation of the Surface Temperature Time Series through Fuzzy Measures

Devi

Chattopadhyay

2023

Axioms

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This paper reports a study to interpret the surface temperature based on time series and fuzzy measures. We demonstrated a method to identify the uncertainty around the surface temperature data concerning the summer monsoon in India. The random variables were standardized, and the Dempster-Shafer Theory was used to generate common goals. Two criteria, represented as fuzzy numbers, were used for this purpose. We constructed three polynomials to illustrate a functional connection between time series and the measure of joint belief. The analysis of the obtained results showed that the certainty increased over time. It confirmed that the degree of the evidence is a more predictable parameter at a more extended period.

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“…where Σ denotes the sum over possible values of the variable. Claude Shannon introduced the concept of information entropy in his 1948 paper "A Mathematical Theory of Communication" and that is why it is also referred to as Shannon entropy [130][131][132][133][134][135]. Shannon entropy is a measure of uncertainty in statistical thermodynamics theory.…”

Section: Introductionmentioning

confidence: 99%

Realization of Bounce in a Modified Gravity Framework and Information Theoretic Approach to the Bouncing Point

Saha

Chattopadhyay

2023

Universe

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In this work, we report a study on bouncing cosmology with modified generalized Chaplygin Gas (mgCG) in a bulk viscosity framework. Reconstruction schemes were demonstrated in Einstein and modified f(T) gravity framework under the purview of viscous cosmological settings. We also took non-viscous cases into account. We studied the equation of state (EoS) parameter under various circumstances and judged the stability of the models through the sign of the squared speed of sound. We observed the mgCG behaving like avoidance of Big Rip in the presence of bulk viscosity at the turnaround point and in non-viscous cases, a phantom-like behavior appears. The turnaround point equation of state parameter crosses the phantom boundary, violating NEC. The role of the mgCG’s model parameters was also investigated before and after the bounce. A Hubble flow dynamics was carried out and, it was revealed that mgCG is capable of realizing an inflationary phase as well as an exit from inflation. An f(T) gravitational paradigm was also considered, where the mgCG density was reconstructed in the presence of bulk viscosity. The role of the parameters associated with the bouncing scale factor, describing how fast the bounce takes place, was also studied in this framework. Finally, the reconstructed mgCG turned out to be stable against small perturbations irrespective of the presence of bulk viscosity and modified gravity scenario. Finally, the reconstruction scheme was assessed using statistical analysis, Shannon entropy.

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Exploring of the summer monsoon rainfall around the Himalayas in time domain through maximization of Shannon entropy

Cited by 7 publications

References 46 publications

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

Efficacy of linear multiple regression and artificial neural network for long-term rainfall forecasting in Western Australia

An Interpretation of the Surface Temperature Time Series through Fuzzy Measures

Realization of Bounce in a Modified Gravity Framework and Information Theoretic Approach to the Bouncing Point

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