Suspended sediment transport during tropical‐cyclone floods in Fiji

Flow records, rising‐stage sediment samplers, and a sand suspension model are used to examine suspended sediment concentrations during major floods caused by tropical cyclones TC Joni and TC Kina in the Rewa River, Fiji. The highest concentrations of total suspended solids were measured during the early stages of TC Kina. The suspension model predicts higher sand concentrations for TC Kina compared with TC Joni because of the larger slope and higher shear stresses during Kina. Extremely high wash load concentrations early in TC Kina are at least partly due to remobilization of fine sediment deposited during the earlier TC Joni flood. Samples from the TC Kina had volumetric concentrations larger than 5%, indicating hyperconcentrated streamflows. Mass‐density shear stresses in the hyperconcentrated flows are up 1·6 times larger than clear‐water shear stresses, but they occur early during low stages of the flood and probably do not result in severe bed erosion. Copyright © 2002 John Wiley & Sons, Ltd.     

Magnetostatic stiff-fluid model of cylindrical symmetry in general relativity

We study the static stiff-fluid model for perfect fluid distributions in the presence of incident magnetic field. The magnetic field is surrounded by static stiff fluid of infinite electric conductivity and it is due to the electric current flowing along theZ-axis. The various physical and geometrical properties together with the state of model in absence of magnetic field are also discussed.     

An LRS Bianchi Type I Bulk Viscous Fluid String Cosmological Model in General Relativity

An LRS Bianchi type I string dust cosmological model with and without bulk viscosity following a method used by Letelier and Stachel, is investigated. To get a determine solution, it is assumed that σ∝θ whereσ is shear and θ is scalar of expansion and which leads to A =αBⁿ were n is a constant. The physical and geometrical aspects of the model are also discussed. This revised version was published online in July 2006 with corrections to the Cover Date.     

Some inhomogeneous cosmological models of plane symmetry with electromagnetic field

Some inhomogeneous cosmological models of plane symmetry in presence of electromagnetic field have been obtained. Various physical and geometrical properties of the models with some special cases are also discussed.     

Inhomogeneous viscous fluid cosmological model with electromagnetic field in general relativity

The object of this paper is to investigate the behaviour of electromagnetic field in inhomogeneous cosmological models obtained for viscous fluid distributions. The various particular cases when both the electromagnetic and viscosity are absent, are also discussed.     

Digital soil mapping in a Himalayan watershed using remote sensing and terrain parameters employing artificial neural network model

Digital soil mapping relies on field observations, laboratory measurements and remote sensing data, integrated with quantitative methods to map spatial patterns of soil properties. The study was undertaken in a hilly watershed in the Indian Himalayan region of Mandi district, Himachal Pradesh for mapping soil nutrients by employing artificial neural network (ANN), a potent data mining technique. Soil samples collected from the surface layer (0–15 cm) of 75 locations in the watershed, through grid sampling approach during the fallow period of November 2015, were preprocessed and analysed for various soil nutrients like soil organic carbon (SOC), nitrogen (N) and phosphorus (P). Spectral indices like Colouration Index, Brightness Index, Hue Index and Redness Index derived from Landsat 8 satellite data and terrain parameters such as Terrain Wetness Index, Stream Power Index and slope using CartoDEM (30 m) were used. Spectral and terrain indices sensitive to different nutrients were identified using correlation analysis and thereafter used for predictive modelling of nutrients using ANN technique by employing feed-forward neural network with backpropagation network architecture and Levenberg–Marquardt training algorithm. The prediction of SOC was obtained with an R² of 0.83 and mean squared error (MSE) of 0.05, whereas for available nitrogen, it was achieved with an R² value of 0.62 and MSE of 0.0006. The prediction accuracy for phosphorus was low, since the phosphorus content in the area was far below the normal P values of typical Indian soils and thus the R² value observed was only 0.511. The attempts to develop prediction models for available potassium (K) and clay (%) failed to give satisfactory results. The developed models were validated using independent data sets and used for mapping the spatial distribution of SOC and N in the watershed.     

Fluoride contamination of groundwater and its seasonal variability in parts of Purulia district,West Bengal,India

The contamination of aquifers by fluoride and arsenic is a major cause of concern in several parts of India. A study has thus been conducted to evaluate the extent and severity of fluoride contamination and also its seasonal variability. Two blocks (Purulia-1 and Purulia-2) were considered for this purpose. Twenty groundwater samples (in each season) were collected from tube wells during the pre-monsoon and post-monsoon seasons. In addition to fluoride, groundwater samples were also analyzed for major cations, anions, and other trace elements. The concentration of fluoride shows significant seasonal variation and ranges between 0.94–2.52 and 0.25–1.43 mg/l during the pre-monsoon and post-monsoon seasons, respectively. In pre-monsoon season, more than 40% of the water samples show fluoride concentrations higher than the WHO limit. However, during the post-monsoon season, none of the groundwater sample shows fluoride concentrations higher than the WHO limit. Lesser concentration during the post-monsoon season is attributed to the dilution effect by the percolating rainwater, which has also been reflected in the form of a decrease in concentrations of other elements. The petrographic studies of the rock samples collected from the study area show that the rocks are mainly composed of plagioclase, orthoclase, and quartz with abundant biotite. The weathering and dissolution of biotite plays an important role in controlling the fluoride concentrations in the groundwater of the study area.     

Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal

The goal of this paper is to evaluate and compare the consistency of GIS-based heuristic and bivariate landslide susceptibility mapping techniques in the Himalayan region, taking the Kulekhani watershed of central Nepal as an example. For this purpose, a heuristic and two statistical bivariate landslide susceptibility mapping methods are applied, and three separate landslide susceptibility zonation maps are produced. The maps are compared using three approaches: landslide density analysis, success rate analysis, and agreed area analysis. A comparison of the values obtained from landslide density analysis and the curves of success rate analysis indicate that the two bivariate methods produce almost identical results, whereas the map produced with the heuristic method differs significantly from the others. On the other hand, the agreed area analysis highlights significant spatial differences in the maps obtained from the three methods. Although the three approaches evaluate the consistency of susceptibility maps, only the agreed area analysis is capable of spatially comparing them. Hence, this approach proves to be more suitable for spatially and quantitatively evaluating the consistency of various landslide susceptibility zonation maps.     

Geoelectrical inversion and evaluation of lithology based on optimized Adaptive Neuro Fuzzy Inference System (ANFIS)

Soft computing tools play a vital role in fixing certain non-linear problems related to the earth. More specifically, digging out the mysteries of subsurface of the earth, the nonlinearity can be converging to assemble an approximate solution which resembles the real characteristics of the earth. Adaptive Neuro Fuzzy Inference System (ANFIS) tool is one of the best soft computing tools to estimate the complex data analysis. ANFIS was applied to estimate the subsurface parameters of earth using the Vertical Electrical Sounding (VES) data. Classifying the lithology based on the resistivity values by ANFIS is employed here in this paper. As the resistivity of each formation varies in range of values, ANFIS tool thus approximates the subsurface features based on effective training. In this study, ANFIS performance was checked with training data, and successively it has been tested with the field data. Optimized ANFIS algorithm provides the necessary tool for predicting the non-linear subsurface features. The best training performance of this soft computing tool efficiently predicts the subsurface lithology. Also the interpreted results show the true resistivity and thickness of the subsurface layers of the earth. The proposed technique was represented in Graphical User Interface (GUI), and the lithological variables are predicted in texture format and linguistic variables.