Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.     

Cyclone Frame Prediction by Gaussian Mixture Modeling of the Three Penultimate Time-Lapse Frames

A fast cyclone frame prediction is proposed in this paper that fits a Gaussian Mixture model on the spatio-temporal data extracted from the three penultimate time-lapse frames, prior to fuzzy regression. Unlike the previous work in Verma and Pal (In: Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1–8, 2010) that models the entire history on a per pixel basis, a single Gaussian mixture is used for fitting the spatio-temporal data within the time-span of the last three frames, making the process faster and more accurate. The increase in accuracy is attributed to the fact that cyclones evolve over time and thus the recent frames give a more meaningful insight into the predictions for the next frame. The number of components in the Gaussian mixture is determined from the occurrence of equally likely modes that correspond to high entropy peaks. Our results on satellite videos of recent cyclones that hit the Indian seas show a high accuracy of frame prediction.     

Glacier surface characteristics derivation and monitoring using Hyperspectral datasets: a case study of Gepang Gath glacier,Western Himalaya

Mountain Glaciers are natural resources of fresh water and these affect the stream flow of the rivers, regional climate and further global climate. Observed trends and projected future evolutions of climate and Cryospheric variables clearly suggest a need to monitor these changes. Accordingly, the article presents the glacier features mapping using Hyperspectral remote sensing imagery. A freely available Hyperion satellite imagery acquired over Gepang Gath glacier in Himachal Pradesh, India is used for the study. Each class is identified based on their surface characteristics of spectral reflectance properties. Identification is simplified by demarcating the study glacier into accumulation and ablation areas through snowline. Accumulation area is characterized with high reflectance clean snow/ice and reduced moderate reflectance Snow/firn. The identification of classes in Hyperion imagery is validated using the spectral library from USGS and ASTER, and field spectra obtained from literature.     

Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach

The study aims at delineating groundwater potential zones using geospatial technology and analytical hierarchy process (AHP) techniques in mining impacted hard rock terrain of Ramgarh and part of Hazaribagh districts, Jharkhand, India. Relevant thematic layers were prepared and assigned weight based on Saaty’s 9-point scale and normalized by eigenvector technique of AHP to identify groundwater prospect in the study area. The weighted linear combination method was applied to prepare the groundwater potential index in geographic information system. Final groundwater prospects were classified as excellent, very good, good, moderate, poor and very poor groundwater potential zones. Study thus revealed that the excellent, very good and good groundwater potential zones, respectively, cover 148.3, 373.66 and 438.86 km² of the study area, whereas the poor groundwater potential zone covers 180.05 km². Validation was done through a receiver operating characteristic curve, which indicated that AHP had good prediction accuracy (AUC = 75.45%).     

Analysing topographical changes in open cast coal-mining region of Patratu,Jharkhand using CARTOSAT-I Stereopair satellite images

This study provides an assessment of changes in the terrain topography due to opencast coal mining in the Patratu region of Jharkhand state during the period of 1962–2007. It demonstrated the potential of digital elevation model (DEM) differencing technique using Cartosat-I satellite (2007) derived DEM with reference to DEM derived from contours obtained from Survey of India topographical map (1962). The topographical changes through DEM differencing revealed positive relief changes (up to 49 m) due to coal-mining overburden dumps. The dumping of overburden near the banks of perennial Damodar River also caused positive topographic changes (up to 20 m) indicating adverse effects on its hydrological regime. Negative relief changes are represents by deep depressions (up to 66 m) created within coal mines due to the extraction of coal. These depression areas within the abundant mines generally become the zones of water accumulation causing wastage of surface and ground water resources.     

Vertical accuracy evaluation of SRTM-GL1, GDEM-V2, AW3D30 and CartoDEM-V3.1 of 30-m resolution with dual frequency GNSS for lower Tapi Basin India

Abstract

Shuttle Radar Topography Mission (SRTM-GL1), Advanced Space Borne Thermal Emission and Reflection Radiometer- Global DEM (GDEM-V2), recently released Advanced Land Observing Satellite (‘DAICHI’) DEM (AW3D30) and Indian National Cartosat-1 DEM v3 (CartoDEM-V3.1) provide free topographic data at a 30-m resolution for Indian peninsula. In this research study, the vertical accuracy of DEM is evaluated for above data-sets and compared with high accuracy dual frequency GNSS of a millimetre accuracy. The extensive field investigation is carried out using a stratified random fast static DGPS survey for collecting 117 high accuracy ground control points in a predominantly agriculture catchment. Further, the effect of land cover, slope and low-lying coastal zone on DEM vertical accuracy was also analysed and presented in this study.     

Estimation of seismic drift demands for frame structures

A process is outlined and evaluated for the estimation of seismic roof and storey drift demands for frame structures from the spectral displacement demand at the first mode period of the structure. The spectral displacement demand is related to the roof drift demand for the multi‐degree‐of‐freedom (MDOF) structure using three modification factors, accounting for MDOF effects, inelasticity effects, and P‐delta effects. Median values and measures of dispersion for the factors are obtained from elastic and inelastic time history analyses of nine steel moment resisting frame structures subjected to sets of ground motions representative of different hazard levels. The roof drift demand is related to the storey drift demands, with the results being strongly dependent on the number of stories and the ground motion characteristics. The relationships proposed in this paper should prove useful in the conceptual design phase, in estimating deformation demands for performance assessment, and in improving basic understanding of seismic behaviour. Copyright © 2000 John Wiley & Sons, Ltd.     

Forecasting shield pressures at a longwall face using artificial neural networks

Leg pressure data from all shields of a longwall face are monitored and recorded in the surface computer. An algorithm is developed to detect peak pressures or periodic roof weightings from these pressure data. The intensities and locations of periodic roof weighting are further analyzed using artificial neural network for forecasting of forthcoming shield pressures. The network was trained using data 153 m (500 ft) of face advance. Shield pressures are forecasted for the successive nine mining cycles or approximately 9 m of face advancement. The results obtained validate the efficacy of the developed model.     

Effect of linear and non-linear mixing on hyper-spectral signatures of snow in the optical region (350–2500 nm)

Study of hyper-spectral behaviour of snow is important to interpret, analyse and validate optical remote sensing observations. To map and understand response of snow-mixed pixels in RS data, field experiments were conducted for linear mixing of external materials (i.e. Vegetation, Soil) with snow, using spectral-radiometer (350–2500 nm). Further, systematic non-linear mixing of snow contaminants (soil, coal, ash) in terms of size and concentration of contaminants is analysed to imitate and understand spectral response of actual field scenarios. Sensitivity of band indices along with absorption peak characteristics provide clues to discriminate the type of contaminants. SWIR region is found to be useful for discriminating size of external contaminants in snow e.g. Avalanche deposited snow from light contaminated forms. Present research provide inputs for mapping snow-mixed pixels in medium/coarse resolution remote sensing RS data (in terms of linear mixing) and suitable wavelength selections for identification and discriminating type/size of snow contaminants (in terms of non-linear mixing).     

RETRACTED ARTICLE: Aspect Sensitivity Considerations in Interpreting Radar Meteor Range-Spread Trail Echo Durations

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