Zoning and monitoring dominant mangrove communities of a part of The Marine National Park, Gulf of Kachchh

Mangroves of the Marine National Park constitute the second largest patch of mangroves in Gujarat, extending up to 11,000 ha, comprising six species of mangroves. Earlier studies carried out using remote sensing data pertained to baseline data generation and mapping and monitoring the mangroves (density-wise) of the Park from 1975 to 1993. Using IRS IC/ID LISS III data (1998–2001) supported by ground data, the distribution of different mangrove communities in the Park has been attempted. Amongst various image-processing techniques, band ratioing followed by supervised classification gave the best result (classification accuracy was 92%).Avicennia community is the most dominant community accounting for more than 70% of the area. TheRhizophora community occupies the inward margins of the creeks and theCeriops community is present in the interior regions. The ecotone between the marsh and mangrove communities has been identified as the transitional mangroves (Avicennia alba, Sueada), representing the transition from the less saline mangrove to the highly saline marsh community. The zoning of the mangroves has also helped in assessing the diversity of the region. Based on the richness of species, three areas, namely Bhains Bid, North-east Dide Ka Bet and South-east Chhad Island have been identified as highly diverse (most suitable area for preservation).     

Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads

A detailed case study of γ-hadron segregation for a ground based atmospheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial Neural Network, Linear Discriminant method, Naive Bayes Classifiers, Support Vector Machines as well as the conventional dynamic supercut method by simulating triggering events with the Monte Carlo method and applied the results to a Cherenkov telescope. It is demonstrated that the Random Forest method is the most sensitive machine learning method for γ-hadron segregation.     

Collisional Penrose process and jets in Kerr naked singularity

In this article, we investigate the possibilities of energy extraction from an over-spinning Kerr spacetime using collisional Penrose process. This phenomenon can produce a high-energy ejecta of particles under certain favourable conditions. Unlike black holes, in this case, the particles endowed with higher energy can escape to infinity. We use this model to explore various possibilities of jet formation in an over-spinning geometry. Primarily we concentrate on the energy extraction associated with collisions taking place on the off-equatorial planes and find the signature of jets from them. We also apply this formalism to a toy model which could be useful in practical astrophysical scenarios. This is motivated from the atomic model where we have considered the decay of a circular orbit because of energy extraction via Penrose mechanism.     

Fuzzy model identification based on cluster estimation for reservoir inflow forecasting

Fuzzy theory appears to be extremely effective at handling dynamic, non‐linear and noisy data, especially when the underlying physical relationships are not fully understood. Since hydrologists are still uncertain about many of the aspects of the physical processes in the watershed, fuzzy theory has proved to be a very attractive tool enabling them to investigate such problems. The effectiveness of the fuzzy model lies in the identification of the antecedent membership function (MF), which is generally addressed through a fuzzy clustering approach. Most of the applications of fuzzy computing in hydrology seem to have selected the clustering algorithm quite arbitrarily. However, it is apparent that, as the antecedent parameters are based solely on the identified clusters, the method used for clustering should certainly have an impact on the overall performance of the model. This paper presents the results of a study conducted to investigate the impact of choice of clustering algorithm on the overall performance of a fuzzy‐based hydrologic model. The research is illustrated through a case study of developing a Takagi–Sugeno fuzzy model for reservoir inflow forecasting in the Narmada basin, India. The model was developed using two popular clustering techniques, namely Gustafson–Kessel (GK) and subtractive clustering (SC), and was extensively evaluated for performance based on various statistical indices. The results show that the model performance is comparable at a 1 h lead forecast. However, it is observed that the GK approach results in a better performance than the SC approach in computing forecasts at higher lead times. The analysis suggest that the GK method clusters the input space based on the actual pattern, since it uses a membership‐grade weighted‐distance measure as the measure of closeness, whereas the SC method classifies the input space more logically according to the magnitude of flow available in the data set. Copyright © 2007 John Wiley & Sons, Ltd.     

Evaluation of impact of industrial effluents on intertidal sediments of a creek

A large amount of material in the form of industrial effluents and urban sewage containing metal contaminants directly enters Dudh creek. Metals on entering the creek tend to settle at the bottom and become part of the sediments. The distribution of metals with depth reflects changes in their supply as well as diagenetic modifications with time. In the present study, an attempt has been made to understand the impact of waste discharged from Tarapur Maharashtra Industrial Development Corporation on Dudh creek. Two cores were collected, one close to the creek mouth and the other in the inner part of the creek. Sediment component, organic carbon and metal (vanadium, copper, nickel, lead, zinc, manganese, iron and aluminium) analyses were carried out on the two cores. The variation in the distribution of sediment components with depth reflects changes in hydrodynamic condition of the creek over the years. Increase in concentration of most of the metals in the upper part of the cores indicates additional input in recent years. The core collected in the inner part of the creek showed higher metal and organic carbon concentration as compared to the downstream core. This was very well supported by distribution of points on an isocon plot. Enrichment factor and index of geoaccumulation were used to find the extent of pollution in sediments of the creek. Correlation and R-mode factor analysis have also been computed to understand the association and source of metal input in sediments of Dudh creek.     

Groundwater arsenic contamination and its health effects in India

During a 28-year field survey in India (1988–2016), groundwater arsenic contamination and its health effects were registered in the states of West Bengal, Jharkhand, Bihar and Uttar Pradesh in the Ganga River flood plain, and the states of Assam and Manipur in the flood plain of Brahamaputra and Imphal rivers. Groundwater of Rajnandgaon village in Chhattisgarh state, which is not in a flood plain, is also arsenic contaminated. More than 170,000 tubewell water samples from the affected states were analyzed and half of the samples had arsenic >10 μg/L (maximum concentration 3,700 μg/L). Chronic exposure to arsenic through drinking water causes various health problems, like dermal, neurological, reproductive and pregnancy effects, cardiovascular effects, diabetes mellitus, diseases of the respiratory and gastrointestinal systems, and cancers, typically involving the skin, lungs, liver, bladder, etc. About 4.5% of the 8,000 children from arsenic-affected villages of affected states were registered with mild to moderate arsenical skin lesions. In the preliminary survey, more than 10,000 patients were registered with different types of arsenic-related signs and symptoms, out of more than 100,000 people screened from affected states. Elevated levels of arsenic were also found in biological samples (urine, hair, nails) of the people living in affected states. The study reveals that the population who had severe arsenical skin lesions may suffer from multiple Bowens/cancers in the long term. Some unusual symptoms, such as burning sensation, skin itching and watering of eyes in the presence of sun light, were also noticed in arsenicosis patients.     

Finite element modelling for evaluation of apparent resistivity over complex structures

The paper presents a computational algorithm designed for efficient modelling of apparent resistivity over complex geological structures, using finite element method. The algorithm can be used to study variations of apparent resistivities using any electrode configuration at any point on the earth’s surface, not necessarily regular. A Schlumberger apparent resistivity sounding curve over a buried anticline, is presented here as an example and compared with the corresponding analytical curve, to demonstrate the correctness of the FEM algorithm. The various potential derivatives required for the computation of apparent resistivities evaluated through different electrode configurations have been obtained by calculating the ‘influence coefficients’ using reciprocal theorems, an approach successfully applied in structural engineering. In essence, a set of self balancing nodal currents, obtained from the appropriate derivative(s) of the shape functions of the elements contributing to the point of observation, is applied as the load vector. The resulting quantities corresponding to the potential distribution in traditional finite element method, then, turn out to be the potential derivatives at the point of observation for different positions of the current electrodes. These are known as influence coefficients. The continuum nature of the domain beyond the region of interest has been modelled by using ‘infinite elements’ across which the potential is assumed to decay exponentially.