An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing

Accurate thematic classification is one of the most commonly desired outputs from remote sensing images. Recent research efforts to improve the reliability and accuracy of image classification have led to the introduction of the Support Vector Classification (SVC) scheme. SVC is a new generation of supervised learning method based on the principle of statistical learning theory, which is designed to decrease uncertainty in the model structure and the fitness of data. We have presented a comparative analysis of SVC with the Maximum Likelihood Classification (MLC) method, which is the most popular conventional supervised classification technique. SVC is an optimization technique in which the classification accuracy heavily relies on identifying the optimal parameters. Using a case study, we verify a method to obtain these optimal parameters such that SVC can be applied efficiently. We use multispectral and hyperspectral images to develop thematic classes of known lithologic units in order to compare the classification accuracy of both the methods. We have varied the training to testing data proportions to assess the relative robustness and the optimal training sample requirement of both the methods to achieve comparable levels of accuracy. The results of our study illustrated that SVC improved the classification accuracy, was robust and did not suffer from dimensionality issues such as the Hughes Effect.     

Horizontal Pullout Resistance for a Group of Two Vertical Plate Anchors in Clays

The horizontal pullout capacity of a group of two rigid strip plate anchors embedded along the same vertical plane in clays, under undrained condition, has been determined. An increase of cohesion with depth has also been incorporated. The analysis has been performed by using an upper bound finite element limit analysis in combination with linear optimization. For different clear spacing (S) between the anchors, the efficiency factor (η_cγ) has been determined to evaluate the group failure load for different values of (1) embedment ratio (H/B), (2) the normalized rate (m) which accounts for a linear increase of cohesion with depth, and (3) normalized unit weight (γH/c_o). The magnitude of the group failure load (1) becomes maximum corresponding to a certain spacing (S_cr) between the anchors, and (2) increases with an increase in the γH/c_o up to a certain value before attaining a certain maximum magnitude. The value of S_cr/B has been found to vary generally between 0.7 and 1.2. The maximum magnitude of η_cγ, associated with the critical spacing, (1) increases generally with increases in H/B, and (2) decreases with an increase in m. For a greater spacing between the anchors, the analysis reveals the development of a local shear zone around the lower anchor plate. The numerical results developed are expected to be useful for purpose of design.     

Stabilization of a Clayey Soil with Fly Ash and Lime: A Micro Level Investigation

Pavement structures on poor soil sub grades show early distresses causing the premature failure of the pavement. Clayey soils usually have the potential to demonstrate undesirable engineering behavior, such as low bearing capacity, high shrinkage and swell characteristics and high moisture susceptibility. Stabilization of these soils is a usual practice for improving the strength. This study reports the improvement in the strength of a locally available cohesive soil by addition of both fly ash and lime. Analysis using X-ray diffraction, scanning electron microscopy, coupled with energy dispersive spectroscopy, thermal gravimetric analysis, zeta potential and pH value test was carried out in order to elucidate the stabilization mechanism. The micro level analysis confirmed the breaking of montmorrillonite structure present in the untreated clay after stabilization. In the analysis, it was also confirmed that in the stabilization process, pozzolanic reaction dominated over the cation exchange capacity.     

Non-Existence of Five Dimensional Perfect Fluid Cosmological Model in Lyra Manifold

In this paper we have taken an attempt to construct a five dimensional perfect fluid cosmological model within the framework of Lyra manifold. It is found that neither perfect fluid nor dust distributions survive. Finally the exact solutions of the vacuum field equations are obtained.     

Bianchi type VI h perfect fluid cosmological model in f(R,T) theory

In this paper, we have investigated Bianchi type VI _h cosmological model filled with perfect fluid in the framework of f(R,T) gravity, where R is the Ricci scalar and T is the trace of the energy-momentum tensor proposed by Harko et al. (Phys. Rev. D 84:024020, 2011). We have obtained the cosmological models by solving the field equations. Some physical behaviors of the model are also studied.     

Projected 21st century trends in hydroclimatology of the Tahoe basin

With down-scaled output from two General Circulation Models (the Geophysical Fluid Dynamics Laboratory, or GFDL, and the Parallel Climate Model, or PCM) and two emissions scenarios (A2 and B1), we project future trends in temperature and precipitation for the Tahoe basin. With the GFDL, we also project drought conditions and (through the use of a distributed hydrologic model) flood frequency. The steepest trend (GFDL with A2) indicates a 4–5°C warming by the end of the 21st century. Trends in annual precipitation are more modest with a dip in the latter half of the 21st century indicated by the GFDL/A2 case, but not the others. Comparisons with the Palmer Drought Severity Index show that drought will increase, in part due to the declining role of the snowpack as a reservoir for soil moisture replenishment. Analysis of flood frequency for the largest watershed in the basin indicates that the magnitude of the 100-yr flood could increase up to 2.5-fold for the middle third of the century, but decline thereafter as the climate warms and dries. These trends have major implications for the management of land and water resources in the Tahoe basin, as well as for design and maintenance of infrastructure.     

Effects of climate change on thermal properties of lakes and reservoirs, and possible implications

Meteorologic-driven processes exert large and diverse impacts on lakes’ internal heating, cooling, and mixing. Thus, continued global warming and climate change will affect lakes’ thermal properties, dynamics, and ecosystem. The impact of climate change on Lake Tahoe (in the states of California and Nevada in the United States) is investigated here, as a case study of climate change effects on the physical processes occurring within a lake. In the Tahoe basin, air temperature data show upward trends and streamflow trends indicate earlier snowmelt. Precipitation in the basin is shifting from snow to rain, and the frequency of intense rainfall events is increasing. In-lake water temperature records of the past 38 years (1970–2007) show that Lake Tahoe is warming at an average rate of 0.013°C/year. The future trends of weather variables, such as air temperature, precipitation, longwave radiation, downward shortwave radiation, and wind speed are estimated from predictions of three General Circulation Models (GCMs) for the period 2001–2100. Future trends of weather variables of each GCM are found to be different to those of the other GCMs. A series of simulation years into the future (2000–2040) is established using streamflows and associated loadings, and meteorologic data sets for the period 1994–2004. Future simulation years and trends of weather variables are selected so that: (1) future simulated warming trend would be consistent with the observed warming trend (0.013°C/year); and (2) future mixing pattern frequency would closely match with the historical mixing pattern frequency. Results of 40-year simulations show that the lake continues to become warmer and more stable, and mixing is reduced. Continued warming in the Tahoe has important implications for efforts towards managing biodiversity and maintaining clarity of the lake.