Shyam Bihari Dwivedi

High grade metamorphism in the Bundelkhand massif and its implications on Mesoarchean crustal evolution in central India

The Bundelkhand Gneissic Complex (BnGC) in the central part of the Bundelkhand massif preserves a supracrustal unit which includes pelitic (garnet–cordierite–sillimanite gneiss, garnet–sillimanite gneiss, biotite gneiss and garnet–biotite gneiss) and mafic (hornblende–biotite gneiss and garnetiferous amphibolite) rocks. Granulite facies metamorphism of the complex initiated with breaking down of biotite to produce garnet and cordierite in the pelitic gneisses. Geothermobarometric calculations indicate metamorphic conditions of 720°C/6.2 kbar, followed by a retrograde (687°C/4.9 kbar) to very late retrograde stages of metamorphism (579°C/4.4 kbar) which is supported by the formation of late cordierite around garnet. The P–T conditions and textural relations of the garnet–cordierite-bearing gneiss suggest a retrograde cooling path of metamorphism.

Evaluating the effects of spatial resolution on land use and land cover classification accuracy

The choice of appropriate spatial resolution is a key factor to extract desired information from remotely sensed images. Optical data collected by two different sensors (LISS IV with 5.8 m and Landsat 8-OLI with 30 m spatial resolution respectively) were investigated against the capability to classify accurately into distinct land use and land cover (LULC) classes. To evaluate the quality of training samples class separability analysis using transformed divergence (TD) method was performed. Furthermore, supervised maximum likelihood classifier (MLC) was used to carry out LULC classification. The results indicated that the overall accuracy 83.28% and Kappa coefficient 0.805 for LISS IV image was found higher in comparison to Landsat 8-OLI image having overall accuracy 77.93% and Kappa coefficient 0.742 respectively.

Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout

The simulation-optimization approach is often used to solve water resource management problem although repeated use of the simulation model enhances the computational load. In this study, Artificial Neural Network (ANN) and Bagged Decision Trees (BDT) models were developed as an approximator for Analytic Element Method (AEM) based groundwater flow model. Developed ANN and BDT models were coupled with Particle Swarm Optimization (PSO) model to solve the well-field management problem. The groundwater flow model was developed for the study area and used to generate the dataset for the training and testing of the ANN & BDT models. These coupled ANN-PSO & BDT-PSO models were employed to find the optimal design and cost of the new well-field system by optimizing discharge & co-ordinate of wells along with the cost effective layout of piping network. The Minimum Spanning Tree (MST) based model was used to find out the optimal piping network layout and checking the hydraulic constraints in the piping network. The results show that the ANN & BDT models are good approximators of AEM model and they can reduce the computational burden significantly although ANN model performs better than BDT model. The results show that the coupling of piping network model with simulation-optimization model is very significant for finding the cost effective and realistic design of the new well-field system.

Hydrological Planning of Watershed of RGSC, Mirzapur, UP, Using GIS Techniques

Water is the remarkable natural resource which is essential for every form of life on the planet “Earth,” and nowadays India is facing major water scarcity problems. These problems are due to climate change and urbanization. As the urbanization is increasing, the percentage of impervious land is increasing which is leading to increase in urban runoff and lesser recharge of underlying aquifers, leading to water scarcity at local scale as runoff is not tapped and utilized. Watershed modeling is considered as one of the most important aspects of planning and development for natural resources for water conservation measures. Watershed modeling is useful for completing and implementing plans, programs, and projects to sustain and increase watershed utilities that directly affect the biotic and abiotic communities within watershed boundary. In this paper, we have taken Rajiv Gandhi South Campus (RGSC), Barkachha, which is the extension of Banaras Hindu University and facing serious water scarcity problems. Here, firstly runoff was calculated by SCS-CN method using 10 years of rainfall data, and then flow accumulation and sink map were created using DEM. Watershed investigation has been done to suggest some hydraulic structures using flow accumulation, sink map, and runoff data, for the study area, mainly to enhance the availability of water for agricultural activity and university development.

Occurrence of wagnerite in Mg–Al granulites of Sonapahar, Meghalaya

We report for the first time the occurrence of rare phosphate wagnerite as a stable phase from the Mg–Al granulites of Sonapahar. The wagnerite bearing assemblages consist of the spinel, phlogopite, brucite and corundum. The wagnerite appears in the Mg–Al granulites due to the break-down of spinel and fluorapatite. The mineral chemistry of the phases has been discussed from the EPMA data, which reveals that the fluorine content of the wagnerite is relatively low due to the exchange of F to coexisting phases. The major oxide analysis of the rocks show the low content of Ca, which is the requisite for the occurrences of wagnerite.

Crop variables estimation by adaptive neuro-fuzzy inference system using bistatic scatterometer data

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.