Radioelement distribution in river,beach, and offshore areas and their significance to Chavara placer deposit,Southern Kerala Coast of India

The radioelement and heavy mineral distribution in river, beach and innershelf areas of the southern Kerala coast is related to placer mineral concentration on the beaches at Chavara. Southern Kerala rivers—Neyyar, Karamana and Vamanapuram—transport higher amounts of radioactive elements than the larger Kallada River due to higher radioactive minerals in the hinterland rocks. Coastal configurations and the seasonal longshore current pattern seems to control along-shore distribution of minerals. The proposed model for placer concentration suggests that the energy difference and seasonal current direction along this coast is important.     

Field and textural relationship in pelitic schists and gneisses from the area around Mangpu, Darjeeling district, West Bengal

The metasedimentary rocks of the area around Mangpu constitute a portion of the hinge zone of the northern limb of the major synform of Lower Darjeeling Himalaya. The rocks display evidences of multiple deformation and at least three major phases of deformation have been recognized. The time relations between the phases of deformation (D₁, D₂, D₃) and metamorphic crystallization reveal a single major prograde metamorphic event that initiated with the D₁ deformation and finally outlasted it. The earlier phase of this metamorphism is essentially regional syn-tectonic lowgrade (greenschist facies) which may be designated (M₁, early). This was followed by regional static metamorphism (M₁, late) in the post-tectonic phase between D₁ and D₂ deformations (upper green schist and amphibolite facies). This M1 metamorphism is superposed by later retrogressive metamorphism (M₂) during the D₂ and D₃ deformations (lower greenschist facies). Within the study area four isograds have been delineated by the first appearance of index minerals in the pelitic schists and gneiss which display Barrovian type of metamorphism.     

Establishing a predictive performance indicator for real‐time hybrid simulation

Real‐time hybrid simulation (RTHS) is increasingly being recognized as a powerful cyber‐physical technique that offers the opportunity for system evaluation of civil structures subject to extreme dynamic loading. Advances in this field are enabling researchers to evaluate new structural components/systems in cost‐effective and efficient ways, under more realistic conditions. For RTHS, performance metric clearly needs to be developed to predict and evaluate the accuracy of various partitioning choices while incorporating the dynamics of the transfer system, and computational/communication delays. In addition, because of the dynamics of the transfer system, communication delays, and computation delays, the RTHS equilibrium force at the interface between numerical and physical substructures is subject to phase discrepancy. Thus, the transfer system dynamics must be accommodated by appropriate actuator controllers. In this paper, a new performance indicator, predictive performance indicator (PPI), is proposed to assess the sensitivity of an RTHS configuration to any phase discrepancy resulting from transfer system dynamics and computational/communication delays. The predictive performance indicator provides a structural engineer with two sets of information as follows: (i) in the absence of a reference response, what is the level of fidelity of the RTHS response? and (ii) if needed, what partitioning adjustments can be made to effectively enhance the fidelity of the response? Moreover, along with the RTHS stability switch criterion, this performance metric may be used as an acceptance criteria for conducting single‐degree‐of‐freedom RTHS. Copyright © 2014 John Wiley & Sons, Ltd.     

Quality characterization of groundwater in Koilsagar project area,Mahabubnagar District,Andhra Pradesh,India

Studies of groundwater chemistry in the Koilsagar project area of Andhra Pradesh indicate that the waters are sodium bicarbonate, sodium chloride, mixed cationic-mixed anionic, mixed cationic Na dominating bicarbonate, and mixed cationic Ca dominating bicarbonate types. Of them, sodium bicarbonate and mixed cationic Mg dominating bicarbonate types of waters are more prevalent.Isocone mapping of specific conductance indicates that the ionic concentration increases from east to west in the area. Graphical treatment of chemical data reveals that, in general, the area has basic water, whereas the left flank canal area is dominated by secondary alkaline water, and Pallamarri and Pedda Rajmur villages have strongly acidic waters. Ion-exchange studies show that cation-anion exchanges exist all over the area except for two places, which have a base exchange hardened type of water.Graphical representation further shows that most of the area has medium salinity-low sodium (C₂S₁) water useful for irrigation purposes. High salinity-low sodium (C₃S₁) and high salinity-medium sodium (C₃S₂) waters are present in some areas, which need adequate drainage to overcome the salinity problem.     

Estimation of heavy and opaque mineral contents of beach and offshore placers using rock magnetic techniques

Exploration for placer deposits involves heavy and opaque mineral data that are conventionally obtained using toxic and expensive chemicals and time-consuming and tedious microscopic counting of a large number of grains. In this investigation, we have used rock magnetic properties to obtain estimates of heavy and opaque mineral contents of placers from the SW coast of India. Magnetic susceptibility and other magnetic properties show strong correlations with heavy and opaque (r > 0.87 andr > 0.94; significant at the 1% level) mineral contents. As one or more types of magnetic minerals are invariably present in placers, magnetic properties may be used as a proxy for heavy and opaque mineral contents. This simple, rapid, inexpensive, and nondestructive method may be adopted by those involved in placer exploration to rapidly scan a large number of samples and delineate economically important pockets for more detailed investigations. This method saves considerable time and tedium. Using magnetic properties, rather than radioactivity, as a proxy for heavy and opaque mineral contents is more advantageous because one or more magnetic minerals are always present, but a radioactive mineral may not always be present in placers. An important limitation is that the ratio of magnetic to heavy/opaque mineral contents should not vary widely.     

Observations of Tsunami Impact on the Coast of Kerala,India

The tsunami generated by the December 2004 Sumatra-Andaman earthquake had a devastating effect on some parts of Kerala coast, which is a coast located in southwest India. Results of post-tsunami field surveys carried out to understand the changes in coastal morphology and sediment characteristics in the worst affected Kayamkulam region of Kerala coast are documented in this study. Analysis of offshore bathymetric data indicates the shifting of depth contours towards shore, indicating erosion of sediments and deepening of innershelf due to the tsunami. Depth measurement along the backwater (T-S canal) in the hinterland region indicates siltation due to the inundation of the canal.     

On the interaction of mesoscale eddies and a tropical cyclone in the Bay of Bengal

Natural Hazards - Oceanic eddies span over a wide range of sizes and affect the thermodynamic properties of water column. By modifying the thermal structure of the upper ocean, these eddies...     

Environmetric and GIS techniques for hydrochemical characterization of the Dal lake,Kashmir Himalaya,India

This research explains the background processes responsible for the spatial distribution of hydrochemical properties of the picturesque eutrophic Himalayan Lake, Dal, located in Kashmir valley, India. Univariate and multivariate statistical analyses were used to understand the spatiotemporal variability of 18 hydrochemical parameters comprising of 12,960 observations collected from 30 sampling sites well distributed within the lake at a grid spacing of 1 km² from March 2014 to February 2016. Hierarchical cluster analysis (HCA) grouped all the sampled data into three clusters based on the hydrochemical similarities, Discriminant analysis also revealed the same clusters and patterns in the data, validating the results of HCA. Wilk’s λ quotient distribution revealed the contribution of ions, nutrients, secchi disk transparency, dissolved oxygen and pH in the formation of clusters. The results are in consonance with the Principal Component Analysis of the whole lake data and individual clusters, which showed that the variance is maximally explained by the ionic component (46.82%) followed by dissolved oxygen and pH (9.36%), nitrates and phosphates (7.33%) and Secchi disk transparency (5.98%). Spatial variability of the hydrochemistry of the lake is due to the variations in water depth, lake water dynamics, flushing rate of water, organic matter decomposition, and anthropogenic pressures within and around the Dal lake ecosystem. Overall, the water quality of the lake is unfit for drinking due to the presence of coliform bacteria in the lake waters.     

Stochastic modelling of relative water permeability in vegetative soils with implications on stability of bioengineered slope

Vegetation is known to influence the hydrological state variables, suction \( \left( \psi \right) \) and volumetric water content (\( \theta_{w} \)) of soil. In addition, vegetation induces heterogeneity in the soil porous structure and consequently the relative permeability (\( k_{r} \)) of water under unsaturated conditions. The indirect method of utilising the soil water characteristic curve (SWCC) is commonly adopted for the determination of \( k_{r} \). In such cases, it is essential to address the stochastic behaviour of SWCC, in order to conduct a robust analysis on the \( k_{r} \) of vegetative cover. The main aim of this study is to address the uncertainties associated with \( k_{r} \), using probabilistic analysis, for vegetative covers (i.e., grass and tree species) with bare cover as control treatment. We propose two approaches to accomplish the aforesaid objective. The univariate suction approach predicts the probability distribution functions of \( {\text{k}}_{\text{r}} \), on the basis of identified best probability distribution of suction. The bivariate suction and water content approach deals with the bivariate modelling of the water content and suction (SWCC), in order to capture the randomness in the permeability curves, due to presence of vegetation. For this purpose, the dependence structure of \( \psi \) and \( \theta_{w} \) is established via copula theory, and the \( k_{r} \) curves are predicted with respect to varying levels of \( \psi - \theta_{w} \) correlation. The results showed that the \( k_{r} \) of vegetative covers is substantially lower than that in bare covers. The reduction in \( k_{r} \) with drying is more in tree cover than grassed cover, since tree roots induce higher levels of suction. Moreover, the air entry value of the soil depends on the magnitude of \( \psi - \theta_{w} \) correlation, which in turn, is influenced by the type of vegetation in the soil. \( k_{r} \) is found to be highly uncertain in the desaturation zone of the relative permeability curve. The stochastic behaviour of \( k_{r} \) is found to be most significant in tree covers. Finally, a simplified case study is also presented in order to demonstrate the impact of the uncertainty in \( k_{r} \), on the stability of vegetates slopes. With an increment in the parameter \( \alpha \), factor of safety (FS) is found to decrease. The trend of FS is reverse of this with parameter \( n \). Overall FS is found to vary around 4–5%, for both bare and vegetative slopes.     

Groundwater Potential Mapping Using GIS-Based Hybrid Artificial Intelligence Methods

Groundwater is one of the major valuable water resources for the use of communities, agriculture, and industries. In the present study, we have developed three novel hybrid artificial intelligence (AI) models which is a combination of modified RealAdaBoost (MRAB), bagging (BA), and rotation forest (RF) ensembles with functional tree (FT) base classifier for the groundwater potential mapping (GPM) in the basaltic terrain at DakLak province, Highland Centre, Vietnam. Based on the literature survey, these proposed hybrid AI models are new and have not been used in the GPM of an area. Geospatial techniques were used and geo-hydrological data of 130 groundwater wells and 12 topographical and geo-environmental factors were used in the model studies. One-R Attribute Evaluation feature selection method was used for the selection of relevant input parameters for the development of AI models. The performance of these models was evaluated using various statistical measures including area under the receiver operation curve (AUC). Results indicated that though all the hybrid models developed in this study enhanced the goodness-of-fit and prediction accuracy, but MRAB-FT (AUC = 0.742) model outperformed RF-FT (AUC = 0.736), BA-FT (AUC = 0.714), and single FT (AUC = 0.674) models. Therefore, the MRAB-FT model can be considered as a promising AI hybrid technique for the accurate GPM. Accurate mapping of the groundwater potential zones will help in adequately recharging the aquifer for optimum use of groundwater resources by maintaining the balance between consumption and exploitation.