Effect of seepage on flow and bedforms dynamics

This study, using an experimental approach, focuses on the effect of downward seepage on a threshold alluvial channel morphology and corresponding turbulent flow characteristics. In all the experiments, we observed that the streamwise time‐averaged velocities and Reynolds shear stresses were increased under the influence of downward seepage. Scales of eddy length and eddy turnover time were significantly increased with the application of downward seepage, leading to sediment transport and initiation of bedforms along the channel length. As the amount of seepage discharge increased, eddy length and turnover time were further increased, causing the development of larger bedforms. It was revealed that the geometry of bedforms was linked with the size of eddies. In this work, statistics of bedform dynamics are presented in terms of multi‐scalar bedforms in the presence of seepage. These multi‐scalar ubiquitous bedforms cast a potential impact on flow turbulence as well as stream bed morphology in channels. We used wavelet to analyse temporally lagged spatial bed elevation profiles that were obtained from a set of laboratory experiments and synchronized the wavelet coefficients with bed elevation fluctuations at different length scales. A spatial cross‐correlation analysis, based on the wavelet coefficients, was performed on these bed elevation datasets to observe the effect of downward seepage on the dynamic behaviour of bedforms at different length scales. It was found that celerity of bedforms reduced with increase in seepage percentage. Bedform celerity was best approximated by a probability density function such as Rayleigh distribution under varying downward seepage. Further, statistical analysis of physical parameters of bedforms ascertained that the reduction in bedform celerity was a result of increased bedform size. Copyright © 2017 John Wiley & Sons, Ltd.     

Margin-based feature selection for hyperspectral data

A margin-based feature selection approach is explored for hyperspectral data. This approach is based on measuring the confidence of a classifier when making predictions on a test data. Greedy feature flip and iterative search algorithms, which attempts to maximise the margin-based evaluation functions, were used in the present study. Evaluation functions use linear, zero–one and sigmoid utility functions where a utility function controls the contribution of each margin term to the overall score. The results obtained by margin-based feature selection technique were compared to a support vector machine-based recurring feature elimination approach. Two different hyperspectral data sets, one consisting of 65 bands (DAIS data) and other with 185 bands (AVIRIS data) were used. With digital airborne imaging spectrometer (DAIS) data, the classification accuracy by greedy feature flip algorithm and sigmoid utility function was 93.02% using a total of 24 selected features in comparison to an accuracy of 91.76% with full set of 65 features. The results suggest a significant increase in classification accuracy with 24 selected features. The classification accuracy (93.4%) achieved by the iterative search margin-based algorithm with 20 selected features using sigmoid utility function is also significantly more accurate than that achieved with 65 features. To judge the usefulness of margin-based feature selection approaches, another hyperspectral data set consisting of 185 features was used. A total of 65 selected features were used to evaluate the performance of margin-based feature selection approach. The results suggest a significantly improved performance by greedy feature flip-based feature selection technique with this data set also. This study also suggest that margin-based feature selection algorithms provide a comparable performance to support vector machine-based recurring feature elimination approach.     

The M w 8.6 Indian Ocean earthquake on 11 April 2012: Coseismic displacement, Coulomb stress change and aftershocks pattern

The M _w 8.6 Indian Ocean earthquake occurred on April 11, 2012 near the NW junction of three plates viz. Indian, Australian and Sunda plate, which caused widespread coseismic displacements and Coulomb stress changes. We analyzed the GPS data from three IGS sites PBRI, NTUS & COCO and computed the coseismic horizontal displacements. In order to have in-depth understanding of the physics of earthquake processes and probabilistic hazard, we estimated the coseismic displacements and associated Coulomb stress changes from two rectangular parallel fault geometries, constrained by Global Positioning System (GPS) derived coseismic displacements. The Coulomb stress changes following the earthquake found to be in the range of 5 to ?4 bar with maximum displacement of ～11 m near the epicenter. We find that most of the aftershocks occurred in the areas of increased Coulomb stress and concentrated in three clusters. The temporal variation of the aftershocks, not conformed to modified Omori’s law, speculating poroelastic processes. It is also ascertained that the spatio-temporal transient stress changes may promote the occurrence of the subsequent earthquakes and enhance the seismic risk in the region.     

Application of numerical modeling for groundwater flow and contaminant transport analysis in the basaltic terrain, Bagalkot, India

A three-dimensional steady-state finite difference groundwater flow model is used to quantify the groundwater fluxes and analyze the subsurface hydrodynamics in the basaltic terrain by giving particular emphasis to the well field that supplies domestic, agricultural, and industrial needs. The alluvial aquifer of the Ghatprabha River comprises shallow tertiary sediment deposits underlain by peninsular gneissic complex of Archean age, located in the central–eastern part of the Karnataka in southern India. Integrated hydrochemical, geophysical, and hydrogeological investigations have been helped in the conceptualization of groundwater flow model. Hydrochemical study has revealed that groundwater chemistry mainly controlled by silicate weathering in the study area. Higher concentration of TDS and NO₃-N are observed, due to domestic, agriculture, and local anthropogenic activities are directed into the groundwater, which would have increased the concentration of the ions in the water. Groundwater flow model is calibrated using head observations from 23 wells. The calibrated model is used to forecast groundwater flow pattern, and anthropogenic contamination migration under different scenarios. The result indicates that the groundwater flows regionally towards the south of catchment area and the migration of contamination would be reached in the nearby well field in less than 10 years time. The findings of these studies are of strong relevance to addressing the groundwater pollution due to indiscriminate disposal practices of hazardous waste in areas located within the phreatic aquifer. This study has laid the foundation for developing detailed predictive groundwater model, which can be readily used for groundwater management practices.     

Modeling of Oblique Load Test on Batter Pile Group Based on Support Vector Machines and Gaussian Regression

This paper evaluates the potential of two machine learning approaches i.e. Support vector machine (SVR) and Gaussian processes (GP) regression to model the oblique load capacity of batter pile groups. Linear regression was used to compare the performance of both SVR and GP based regression approaches to model the oblique load. Data set used consists of 147 samples obtained from the laboratory experiments. Out of the total sample size, 105 randomly selected samples were used for training whereas remaining 42 were used for testing the models. Input data set consist of angle of oblique load, pile length, sand relative density, number of vertical piles, number of batter piles where as oblique load was considered as output. Two kernel functions i.e. Polynomial and radial based kernel function were used with both SVR and GP regression. A comparison of results suggest that radial basis function based SVR approach works well in comparison to GP and linear regression based approaches and it could successfully be employed in modelling the oblique load capacity of batter pile groups. Parametric analysis and sensitivity analysis suggest that loading angle, pile length and number of batter pile were important in prediction of oblique load.     

Climatology of the Eastern Arabian Sea during the last glacial cycle reconstructed from paired measurement of foraminiferal δO and Mg/Ca

Paired measurements of Mg/Ca and δ¹⁸O of Globigerenoides sacculifer from an Eastern Arabian Sea (EAS) sediment core indicate that sea-surface temperature (SST) varied within 2°C and sea-surface salinity within 2 psu during the last 100 ka. SST was coldest (∼ 27°C) during Marine Isotope Stage (MIS) 4 and 2. Sea-surface salinity was highest (∼ 37.5 psu) during most of the last glacial period (∼ 60–18 ka), concurrent with increased δ¹⁸O_G.sacculifer and C/N ratios of organic matter and indicative of sustained intense winter monsoons. SST time series are influenced by both Greenland and Antarctic climates. However, the sea-surface salinity time series and the deglacial warming in the SST record (beginning at ∼ 18 ka) compare well with the LR04 benthic δ¹⁸O-stack and Antarctic temperatures. This suggests a teleconnection between the climate in the Southern Hemisphere and the EAS. Therefore, the last 100-ka variability in EAS climatology appears to have evolved in response to a combination of global climatic forcings and regional monsoons. The most intense summer monsoons within the Holocene occurred at ∼ 8 ka and are marked by SST cooling of ∼ 1°C, sea-surface salinity decrease of 0.5 psu, and δ¹⁸O_G.sacculifer decrease of 0.2‰.     

Estimation of Indian summer monsoon rainfall using Kalpana-1 VHRR data and its validation using rain gauge and GPCP data

In the present study, an attempt has been made to estimate and validate the daily and monthly rainfall during the Indian summer monsoon seasons of 2008 and 2009 using INSAT (Indian National Satellite System) Multispectral Rainfall Algorithm (IMSRA) technique utilizing Kalpana-1 very high resolution radiometer (VHRR) measurements. In contrary to infrared (IR), microwave (MW) rain rates are based on measurements that sense precipitation in clouds and do not rely merely on cloud top temperature. Geostationary satellites provide broad coverage and frequent refresh measurements but microwave measurements are accurate but sparse. IMSRA technique is the combination of the infrared and microwave measurements which make use of the best features of both IR- and MW-based rainfall estimates. The development of this algorithm included two major steps: (a) classification of rain-bearing clouds using proper cloud classification scheme utilizing Kalpana-1 IR and water vapor (WV) brightness temperatures (Tb) and (b) collocation of Kalpana-1 IR brightness temperature with Tropical Rainfall Measuring Mission (TRMM)-Precipitation Radar (PR) surface rain rate and establishment of a regression relation between them. In this paper, the capability of IMSRA as an operational algorithm has been tested for the two monsoon seasons 2008 and 2009. For this, IMSRA has been used to estimate daily and monthly rainfall and has been intercompared on daily and monthly scales with TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 V6 product and Global Precipitation Climatology Project (GPCP) rain product during these two monsoon years. The daily and monthly IMSRA rainfall has also been validated against ground-based observations from Automatic Weather Station (AWS) Rain Gauge and Buoy data. The algorithm proved to be in good correlation with AWS data over land up to 0.70 for daily rain estimates except orographic regions like North-East and South-West India and 0.72 for monthly rain estimates. The validation with Buoys gives the reasonable correlation of 0.49 for daily rain estimates and 0.66 for monthly rain estimates over Tropical Indian Ocean.