Evaluating the effect of active ions on the early performance of soft clay solidified by modified biomass waste-rice husk ash     

Chen  Ruifeng  Congress  Surya Sarat Chandra  Cai  Guojun  Zhou  Ruixian  Xu  Jingmin  Duan  Wei  Liu  Songyu 《Acta Geotechnica》2023,18(2):1039-1056

Acta Geotechnica - Soft clay generally cannot be directly used as subgrade material due to its poor engineering characteristics. The application of rice husk ash (RHA) for the solidification of...  相似文献   

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

Prakash  Kumar Ravi  Nigam  Tanuja  Pant  Vimlesh  Chandra  Navin 《Natural Hazards》2021,106(3):1981-2001

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...  相似文献   

Kelvin–Helmholtz instability of magnetohydrodynamic waves propagating on solar surges     

I. Zhelyazkov  R. Chandra  A. K. Srivastava  T. Mishonov 《Astrophysics and Space Science》2015,356(2):231-240

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Gravitational collapse of a circularly symmetric star in an anti-de Sitter spacetime     

Ranjan Sharma  Shyam Das  Farook Rahaman  Gopal Chandra Shit 《Astrophysics and Space Science》2015,357(1):1-9

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Performance study of various spatial indexes on 3D geo-data in Geo-RDBMS     

Harish Chandra Karnatak  Vaibhav Kumar 《国际地球制图》2015,30(6):633-649

In the emerging era of information and communication technologies, geotechnology is one of the fastest growing fields. Geo-RDBMS is very important and evolving aspect for GIS, as it can manage large volume of spatial data inside RDBMS. The utilization of RDBMS for geospatial data was one of the important focuses of GIS professionals in last decades to store and manage 2D geo-data. However, the support for 3D geo-data inside RDBMS is still limited and is a challenging task for RDBMS providers. In this study, data organization and performance assessment of 3D geo-data inside RDBMS are carried out. In this process, various file-based 3D data models such as CityGML, COLLADA and KML are migrated to geo-RDBMS to bring entire 3D geo-data in common platform. Various spatial indexing techniques viz. R-Tree, B-Tree, GiST, etc. are applied on these 3D data models and best indexing techniques are studied for 3D GIS operations.  相似文献   

Axi-symmetric Analysis of Geosynthetic-reinforced Granular Fill-soft Soil System with Group of Stone Columns   总被引：1，自引：1，他引：0  

Kousik Deb  P. K. Basudhar  S. Chandra 《Geotechnical and Geological Engineering》2010,28(2):177-186

The paper presents a mechanical model to predict the behavior of geosynthetic-reinforced granular fill resting over soft soil improved with group of stone columns subjected to circular or axi-symmetric loading. The saturated soft soil has been idealized by spring-dashpot system. Pasternak shear layer and rough elastic membrane represent the granular fill and geosynthetic reinforcement layer, respectively. The stone columns are idealized by stiffer springs. The nonlinear behavior of granular fill and soft soil is considered. Consolidation of the soft soil due to inclusion of stone columns has also been included in the model. The results obtained by using the present model when compared with the reported results obtained from laboratory model tests shows very good agreement. The effectiveness of geosynthetic reinforcement to reduce the maximum and differential settlement and transfer the stress from soft soil to stone columns is highlighted. It is observed that the reduction of settlement and stress transfer process are greatly influenced by stiffness and spacing of the stone columns. It has been further observed that for both geosynthetic-reinforced and unreinforced cases, the maximum settlement does not change if the ratio between spacing and diameter of stone columns is greater than 4.  相似文献   

Use of objective analysis to estimate winter temperature and precipitation at different stations over western Himalaya     

Jagdish Chandra Joshi  Ashwagosha Ganju 《Journal of Earth System Science》2010,119(5):597-602

Temperature and fresh snow are essential inputs in an avalanche forecasting model. Without these parameters, prediction of avalanche occurrence for a region would be very difficult. In the complex terrain of Himalaya, nonavailability of snow and meteorological data of the remote locations during snow storms in the winter is a common occurrence. In view of this persistent problem present study estimates maximum temperature, minimum temperature, ambient temperature and precipitation intensity on different regions of Indian western Himalaya by using similar parameters of the neighbouring regions. The location at which parameters are required and its neighbouring locations should all fall in the same snow climatic zone. Initial step to estimate the parameters at a location, is to shift the parameters of neighbouring regions at a reference height corresponding to the altitude of the location at which parameters are to be estimated. The parameters at this reference height are then spatially interpolated by using Barnes objective analysis. The parameters estimated on different locations are compared with the observed one and the Root Mean Square Errors (RMSE) of the observed and estimated values of the parameters are discussed for the winters of 2007–2008.  相似文献   

Statistical analysis of Soft X-ray solar flares during solar cycles 21, 22 and 23     

Navin Chandra Joshi  Neeraj Singh Bankoti  Seema Pande  Bimal Pande  Wahab Uddin  Kavita Pandey 《New Astronomy》2010,15(6):538-546

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A copula-based closed-form binary logit choice model for accommodating spatial correlation across observational units   总被引：4，自引：2，他引：2  

Chandra R. Bhat  Ipek N. Sener 《Journal of Geographical Systems》2009,11(3):243-272

This study focuses on accommodating spatial dependency in data indexed by geographic location. In particular, the emphasis is on accommodating spatial error correlation across observational units in binary discrete choice models. We propose a copula-based approach to spatial dependence modeling based on a spatial logit structure rather than a spatial probit structure. In this approach, the dependence between the logistic error terms of different observational units is directly accommodated using a multivariate logistic distribution based on the Farlie-Gumbel-Morgenstein (FGM) copula. The approach represents a simple and powerful technique that results in a closed-form analytic expression for the joint probability of choice across observational units, and is straightforward to apply using a standard and direct maximum likelihood inference procedure. There is no simulation machinery involved, leading to substantial computation gains relative to current methods to address spatial correlation. The approach is applied to teenagers’ physical activity participation levels, a subject of considerable interest in the public health, transportation, sociology, and adolescence development fields. The results indicate that failing to accommodate heteroscedasticity and spatial correlation can lead to inconsistent and inefficient parameter estimates, as well as incorrect conclusions regarding the elasticity effects of exogenous variables.

Ipek N. SenerEmail:

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Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud   总被引：1，自引：0，他引：1  

Murali Krishna Gumma  Pardhasaradhi G. Teluguntla  Adam Oliphant  Jun Xiong  Chandra Giri 《地理信息系统科学与遥感》2020,57(3):302-322

ABSTRACT

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop a high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, 10 time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the three time-periods over 12 months (monsoon: Days of the Year (DOY) 151–300; winter: DOY 301–365 plus 1–60; and summer: DOY 61–150), taking the every 8-day data from Landsat-8 and 7 for the years 2013–2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the five agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N = 2179) in five AEZs. The classification was performed on GEE for each of the five AEZs using well-established knowledge-base and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N = 1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full-resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/.  相似文献