A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Influence of Spatial Scales on the Performance of Saturated Subsurface Flow and Transport Models

Numerical modeling of saturated subsurface flow and transport has been widely used in the past using different numerical schemes such as finite difference and finite element methods. Such modeling often involves discretization of the problem in spatial and temporal scales. The choice of the spatial and temporal scales for a modeling scenario is often not straightforward. For example, a basin-scale saturated flow and transport analysis demands larger spatial and temporal scales than a meso-scale study, which in turn has larger scales compared to a pore-scale study. The choice of spatial-scale is often dictated by the computational capabilities of the modeler as well as the availability of fine-scale data. In this study, we analyze the impact of different spatial scales and scaling procedures on saturated subsurface flow and transport simulations.

Multiphase models for simulating hot and slightly miscible DNAPL (dense non-aqueous phase fluids) in a saturated rock fracture under deformation

In the present study a two dimensional model is first developed to show the behaviour of dense non-aqueous phase liquids (DNAPL) within a rough fracture. To consider the rough fracture, the fracture is imposed with variable apertures along its plane. It is found that DNAPL follows preferential pathways. In next part of the study the above model is further extended for non-isothermal DNAPL flow and DNAPL-water interphase mass transfer phenomenon. These two models are then coupled with joint deformation due to normal stresses. The primary focus of these models is specifically to elucidate the influence of joint alteration due to external stress and fluid pressures on flow driven energy transport and interphase mass transfer. For this, it is assumed that the critical value for joint alteration is associated with external stress and average of water and DNAPL pressures in multiphase system and the temporal and spatial evolution of joint alteration are determined for its further influence on energy transport and miscible phase transfer. The developed model has been studied to show the influence of deformation on DNAPL flow. Further this preliminary study demonstrates the influence of joint deformation on heat transport and phase miscibility via multiphase flow velocities. It is seen that the temperature profile changes and shows higher diffusivity due to deformation and although the interphase miscibility value decreases but the lateral dispersion increases to a considerably higher extent.

Quench dynamics and defect production in the Kitaev and extended Kitaev models

We study quench dynamics and defect production in the Kitaev and the extended Kitaev models. For the Kitaev model in one dimension, we show that in the limit of slow quench rate, the defect density n∼1/√τ, where 1/τ is the quench rate. We also compute the defect correlation function by providing an exact calculation of all independent nonzero spin correlation functions of the model. In two dimensions, where the quench dynamics takes the system across a critical line, we elaborate on the results of earlier work [K. Sengupta, D. Sen, and S. Mondal, Phys. Rev. Lett. 100, 077204 (2008)] to discuss the unconventional scaling of the defect density with the quench rate. In this context, we outline a general proof that for a d-dimensional quantum model, where the quench takes the system through a d−m dimensional gapless (critical) surface characterized by correlation length exponent ν and dynamical critical exponent z, the defect density n∼1/τmν/(zν+1). We also discuss the variation of the shape and spatial extent of the defect correlation function with both the rate of quench and the model parameters and compute the entropy generated during such a quenching process. Finally, we study the defect scaling law, entropy generation and defect correlation function of the two-dimensional extended Kitaev model.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

The precision of regulation in Dictyostelium discoideum: implications for cell-type proportioning in the absence of spatial pattern

We have made careful counts of the exact number of spore, stalk and basal disc cells in small fruiting bodies of Dictyostelium discoideum (undifferentiated amoebae are found only rarely and on average their fraction is 4.96 x 10(-4)). (i) Within aggregates of a given size, the relative apportioning of amoebae to the main cell types occurs with a remarkable degree of precision. In most cases the coefficient of variation (c.v.) in the mean fraction of cells that form spores is within 4.86%. The contribution of stalk and basal disc cells is highly variable when considered separately (c.v.'s upto 25% and 100%, respectively), but markedly less so when considered together. Calculations based on theoretical models indicate that purely cell-autonomous specification of cell, fate cannot account for die observed accuracy of proportioning. Cell-autonomous determination to a prestalk or prespore condition followed by cell type interconversion, and stabilised by feedbacks, suffices to explain the measured accuracy. (ii) The fraction of amoebae that differentiates into spores increases monotonically with the total number of cells. This fraction rises from an average of 73.6% for total cell numbers below 30 and reaches 86.0% for cell numbers between 170 and 200 (it remains steady thereafter at around 86%). Correspondingly, the fraction of amoebae differentiating into stalk or basal disc decreases viith total size. These trends are in accordance with evolutionary expectations and imply that a mechanism for sensing the overall size of the aggregate also plays an essential role in the determination of cell-type proportions.

Evaluation of spatial variation of peak horizontal acceleration and spectral acceleration for south India: a probabilistic approach

In this work, an attempt has been made to evaluate the spatial variation of peak horizontal acceleration (PHA) and spectral acceleration (SA) values at rock level for south India based on the probabilistic seismic hazard analysis (PSHA). These values were estimated by considering the uncertainties involved in magnitude, hypocentral distance and attenuation of seismic waves. Different models were used for the hazard evaluation, and they were combined together using a logic tree approach. For evaluating the seismic hazard, the study area was divided into small grids of size 0.1A degrees A xA 0.1A degrees, and the hazard parameters were calculated at the centre of each of these grid cells by considering all the seismic sources within a radius of 300 km. Rock level PHA values and SA at 1 s corresponding to 10% probability of exceedance in 50 years were evaluated for all the grid points. Maps showing the spatial variation of rock level PHA values and SA at 1 s for the entire south India are presented in this paper. To compare the seismic hazard for some of the important cities, the seismic hazard curves and the uniform hazard response spectrum (UHRS) at rock level with 10% probability of exceedance in 50 years are also presented in this work.

Inverse models to analyze the spatiotemporal variations of chemical weathering fluxes in a granito-gneissic watershed: Mule Hole, South India

Water-rock reactions are driven by the influx of water, which are out of equilibrium with the mineral assemblage in the rock. Here a mass balance approach is adopted to quantify these reactions. Based on field experiments carried out in a granito-gneissic small experimental watershed (SEW), Mule Hole SEW (similar to 4.5 km(2)), quartz, oligoclase, sericite, epidote and chlorite are identified as the basic primary minerals while kaolinite, goethite and smectite are identified as the secondary minerals. Observed groundwater chemistry is used to determine the weathering rates, in terms of `Mass Transfer Coefficients' (MTCs), of both primary and secondary minerals. Weathering rates for primary and secondary minerals are quantified in two steps. In the first step, top red soil is analyzed considering precipitation chemistry as initial phase and water chemistry of seepage flow as final phase. In the second step, minerals present in the saprolite layer are analyzed considering groundwater chemistry as the output phase. Weathering rates thus obtained are converted into weathering fluxes (Q(weathering)) using the recharge quantity. Spatial variability in the mineralogy observed among the thirteen wells of Mule Hole SEW is observed to be reflected in the MTC results and thus in the weathering fluxes. Weathering rates of the minerals in this silicate system varied from few 10 mu mol/L (in case of biotite) to 1000 s of micromoles per liter (calcite). Similarly, fluxes of biotite are observed to be least (7 +/- 5 mol/ha/yr) while those of calcite are highest (1265 791 mol/ha/yr). Further, the fluxes determined annually for all the minerals are observed to be within the bandwidth of the standard deviation of these fluxes. Variations in these annual fluxes are indicating the variations in the precipitation. Hence, the standard deviation indicated the temporal variations in the fluxes, which might be due to the variations in the annual rainfall. Thus, the methodology adopted defines an inverse way of determining weathering fluxes, which mainly contribute to the groundwater concentration. (C) 2011 Elsevier B.V. All rights reserved.

Asymptotics of the invariant measure in mean field models with jumps

We consider the asymptotics of the invariant measure for the process of spatial distribution of N coupled Markov chains in the limit of a large number of chains. Each chain reflects the stochastic evolution of one particle. The chains are coupled through the dependence of transition rates on the spatial distribution of particles in the various states. Our model is a caricature for medium access interactions in wireless local area networks. Our model is also applicable in the study of spread of epidemics in a network. The limiting process satisfies a deterministic ordinary differential equation called the McKean-Vlasov equation. When this differential equation has a unique globally asymptotically stable equilibrium, the spatial distribution converges weakly to this equilibrium. Using a control-theoretic approach, we examine the question of a large deviation from this equilibrium.

Performance evaluation of chemistry transport models over India

Using continuous and near-real time measurements of the mass concentrations of black carbon (BC) aerosols near the surface, for a period of 1 year (from January to December 2006) from a network of eight observatories spread over different environments of India, a space-time synthesis is generated. The strong seasonal variations observed, with a winter high and summer low, are attributed to the combined effects of changes in synoptic air mass types, modulated strongly by the atmospheric boundary layer dynamics. Spatial distribution shows much higher BC concentration over the Indo-Gangetic Plain (IGP) than the peninsular Indian stations. These were examined against the simulations using two chemical transport models, GOCART (Goddard Global Ozone Chemistry Aerosol Radiation and Transport) and CHIMERE for the first time over Indian region. Both the model simulations significantly deviated from the measurements at all the stations; more so during the winter and pre-monsoon seasons and over mega cities. However, the CHIMERE model simulations show better agreement compared with the measurements. Notwithstanding this, both the models captured the temporal variations; at seasonal and subseasonal timescales and the natural variabilities (intra-seasonal oscillations) fairly well, especially at the off-equatorial stations. It is hypothesized that an improvement in the atmospheric boundary layer (ABL) parameterization scheme for tropical environment might lead to better results with GOCART.

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM+ data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions.

Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

Spiral-wave dynamics in ionically realistic mathematical models for human ventricular tissue: the effects of periodic deformation

We carry out an extensive numerical study of the dynamics of spiral waves of electrical activation, in the presence of periodic deformation (PD) in two-dimensional simulation domains, in the biophysically realistic mathematical models of human ventricular tissue due to (a) ten-Tusscher and Panfilov (the TP06 model) and (b) ten-Tusscher, Noble, Noble, and Panfilov (the TNNPO4 model). We first consider simulations in cable-type domains, in which we calculate the conduction velocity theta and the wavelength lambda of a plane wave; we show that PD leads to a periodic, spatial modulation of theta and a temporally periodic modulation of lambda; both these modulations depend on the amplitude and frequency of the PD. We then examine three types of initial conditions for both TP06 and TNNPO4 models and show that the imposition of PD leads to a rich variety of spatiotemporal patterns in the transmembrane potential including states with a single rotating spiral (RS) wave, a spiral-turbulence (ST) state with a single meandering spiral, an ST state with multiple broken spirals, and a state SA in which all spirals are absorbed at the boundaries of our simulation domain. We find, for both TP06 and TNNPO4 models, that spiral-wave dynamics depends sensitively on the amplitude and frequency of PD and the initial condition. We examine how these different types of spiral-wave states can be eliminated in the presence of PD by the application of low-amplitude pulses by square- and rectangular-mesh suppression techniques. We suggest specific experiments that can test the results of our simulations.