Browsing by Author "S.B. Dwivedi"

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Crop variables estimation by adaptive neuro-fuzzy inference system using bistatic scatterometer data
(Institute of Electrical and Electronics Engineers Inc., 2016) D.K. Gupta; R. Prasad; P. Kumar; V.N. Mishra; P.K.S. Dikshit; S.B. Dwivedi; A. Ohri; R.S. Singh; V. Srivastav; Prashant Kumar Srivastava
The aim of present study is to estimate the crop variables by means of high performing technique like adaptive neuro-fuzzy inference system (ANFIS) using the bistatic scatterometer data. An outdoor 4×4 m2 crop bed of rice crop was prepared for performing all the experiments. The bistatic measurements were carried out over the entire growing stages of the rice crop from transplanting to ripening stage at the angular range of 200 to 700 with the steps 50 at both HH- and VV-polarizations in X-band. The ANFIS algorithm was used for the estimation of rice crop variables. The observed bistatic scattering coefficients and crop variables (biomass, leaf area index, plant height and chlorophyll content) were interpolated with the phenological stages of the rice crop. The 80% data sets were used for training while the remaining 20% were kept separately for the testing purposes. The bistatic scattering coefficients were used as the input data sets and the rice crop variables as the target data sets of fuzzy inference system for both the polarizations. The estimated values were found closer to the observed values of rice crop variables that indicate a satisfactory performance of ANFIS algorithm for estimating rice crop variables. © 2015 IEEE.
Fluoride contamination in southern block of Sonbhadra district, Uttar Pradesh, India
(India Meteorological Department, 2020) Ravi Shanker Patel; S.K. Tiwari; S.B. Dwivedi; D. Mohan
[No abstract available]
Geochemical assessment of groundwater quality in Keonjhar City, Odisha, India
(Springer Science and Business Media Deutschland GmbH, 2020) Sughosh Madhav; Ashutosh Kumar; Jyoti Kushawaha; Arif Ahamad; Pardeep Singh; S.B. Dwivedi
This study intended at the recognition of hydrogeochemical processes and groundwater excellence by applying different quality indices for intake and farming functions. Thirty groundwater samples were taken from the Keonjhar city, Odisha, and different hydrogeochemical parameters were analyzed to understand groundwater excellence. In the current work, cation chemistry shows the sequence of Na+ > Ca2+ > Mg2+ > K+ and anions chemistry HCO3−>Cl−>SO42−>NO3. Gibbs plot indicates that groundwater samples are found in rock dominance. Different ratios of the major ions indicate that silicate weathering and anthropogenic activities were the major sources of ions in the groundwater. Factor examination also validates that both natural and anthropogenic actions are contributing ion in the aquifer. Based on diverse indices used for agriculture purpose, the greater part of the groundwater in the investigative region is appropriate for farming function. Groundwater quality is a dynamic process and subject to seasonal and spatial changes, so continuous assessment and monitoring is required. This study provides the severity of NO3 contamination in the study area as 40% of samples show the values more than the permissible limit. So, proper remediation measures are required prior to consumption of groundwater. © 2020, Springer Nature Switzerland AG.
Internally consistent geothermobarometers in the system FeO-MgO-Al2O3-SiO2-H2O involving garnet, cordierite, aluminosilicate and quartz and their application to metapelites
(1997) S.B. Dwivedi; A. Mohan; R.K. Lal
Reactions involving garnet, cordierite, aluminosilicate and quartz in FMASH system are calibrated as geothermobarometers, namely 1/2 Fe-crd + 1/3 Pyrope = 1/2 Mg-crd + 1/3 Almandine (1); 2 Almandine + 4 sillimanite / andalusite + 5 quartz = 3 Fe-crd (2 and 4); and 2 Pyrope + 4 sillimanite/andalusite + 5 quartz = 3 Mg-crd (3 and 5). T = [6170+ 0.031(P-1)-400(XFe-XMg)Crd-A]/(RlnK D+2.812+B) (1) A= 166 X2Mg-506 X2 Fe + 680 XFeXMg+336 (XCa+XMn) (XMg+XFe)-3300 XCa - 358 XMn and B = 1.5 XCa For Fe and Mg end-member reactions involving sillimanite, the following geobarometric formulation is obtained: PFe=1+[{(24.02-6RlnKFe)T-17369}/4.003] (2) PMg=1+[{(7.238-6RlnKMg)T+19640}/3.82] (3) Similarly for reactions involving andalusite, PFe. and PMg expressions are : PFe=1+[{(26.04-6RlnKFe)T-19471}/3.86] (4) PMg=1+[{(9.25-6RlnKMg)T+17536}/3-68] (5).
Non-ideal Mg-Fe binary mixing in cordierite: Constraints from experimental data on Mg-Fe partitioning in garnet and cordierite and a reformulation of garnet-cordierite geothermometer
(1996) S.B. Dwivedi
The non-ideal regular Mg-Fe binary in cordierite has been derived through multivariate linear regression of the expression RT ln KD +(P -1)ΔK1.2980 along with up-dated subregular mixing parameter of almandine-pyrope solution (Hackler and Wood 1989; Herman 1990). The data base used for multivariate analyses consists of published experimental data (n=177) on Mg-Fe partitioning between garnet and cordierite in the P-T range 650-1050°C and 4-12 K. bar. The non-ideality can be approximated by temperature-dependent Margules parameters. The retrieved values of ΔH〈T〉0 and ΔS〈T〉0 of exchange reaction between garnet and cordierite and enthalpy and entropy of mixing of Mg-Fe cordierite were combined with recent quaternary (Fe-Mg-Ca-Mn) mixing data in garnet to obtain the geothermometric expressions to determine temperature (T Kelvin): T(HW) = 6832 + 0031(P-l)-{166(XMgGt)2-506(XFe Gt)2 + 680XFeGtXMgGt + 336(XCa + XMn). © Printed in India.
Physico-Mechanical Characteristics of Vindhyan Sandstone, India
(Springer, 2022) V. Chaudhary; A. Srivastav; V.H.R. Pandey; Ashutosh Kainthola; S.K. Tiwari; S.B. Dwivedi; T.N. Singh
Quick and reliable estimation of intact rock strength parameters is vital for the excavation and stability measurement. The present research details the assessment of a few physico-mechanical parameters of sandstone rocks from Eastern India, and their statistical correlation and swift prediction. The dataset consists of 150 experimentally evaluated values for dry density, porosity, uniaxial compressive strength, tensile strength and Young’s modulus. Afterwards, the data were analyzed in the statistical environment “R” for correlation and distribution. For the ease of usage and implementation, density and porosity have been used as explanatory variables for the prediction of strength attributes. Initially, univariate linear regression models were devised, which yielded a coefficient of determination ranging between, 0.5 to 0.73. However, the r2 increased, in a range between 0.69 and 0.74, when multivariate analysis using the same independent variables was performed. In the present work, investigations and analysis have been done to predict the uniaxial compressive strength, tensile strength and Young’s modulus with dry density and porosity. Moreover, the statistical significance of the study has been discussed and compared with previous work. The present research can be used as a means to quickly and economically estimate strength parameters in the absence of a sophisticated testing setup. © 2021, The Institution of Engineers (India).