Application of an Expert System to Predict Maximum Explosive Charge Used Per Delay in Surface Mining

The present paper mainly deals with the prediction of maximum explosive charge used per delay (Q _MAX) using an artificial neural network (ANN) incorporating peak particle velocity (PPV) and distance between blast face to monitoring point (D). One hundred and fifty blast vibration data sets were monitored at different vulnerable and strategic locations in and around major coal producing opencast coal mines in India. One hundred and twenty-four blast vibrations records were used for the training of the ANN model vis-à-vis to determine site constants of various conventional vibration predictors. The other 26 new randomly selected data sets were used to test, evaluate and compare the ANN prediction results with widely used conventional predictors. Results were compared based on coefficient of correlation (R), mean absolute error and mean squared between measured and predicted values of Q _MAX. It was found that coefficient of correlation between measured and predicted Q _MAX by ANN was 0.985, whereas it ranged from 0.316 to 0.762 by different conventional predictor equations. Mean absolute error and mean squared error was also very small by ANN, whereas it was very high for different conventional predictor equations.     

Inclusion of earthquake strong ground motion in a geographic information system-based landslide susceptibility zonation in Garhwal Himalayas

Garhwal Himalayas are seismically very active and simultaneously suffering from landslide hazards. Landslides are one of the most frequent natural hazards in Himalayas causing damages worth more than one billion US$ and around 200 deaths every year. Thus, it is of paramount importance to identify the landslide causative factors to study them carefully and rank them as per their influence on the occurrence of landslides. The difference image of GIS-derived landslide susceptibility zonation maps prepared for pre- and post-Chamoli earthquake shows the effect of seismic shaking on the occurrence of landslides in the Garhwal Himalaya. An attempt has been made to incorporate seismic shaking parameters in terms of peak ground acceleration with other static landslide causative factors to produce landslide susceptibility zonation map in geographic information system environment. In this paper, probabilistic seismic hazard analysis has been carried out to calculate peak ground acceleration values at different time periods for estimating seismic shaking conditions in the study area. Further, these values are used as one of the causative factors of landslides in the study area and it is observed that it refines the preparation of landslide susceptibility zonation map in seismically active areas like Garhwal Himalayas.     

Estimation of Wheat/Rice Residue Burning Areas in Major Districts of Haryana,India, Using Remote Sensing Data

Haryana has emerged as an important state for Rice & Wheat production in India contributing significantly in the central pool. Mechanized combine harvesting technologies, which have become common in Rice Wheat System (RWS) in India, leave behind large quantities of straw in the field for open burning of residue. Besides causing pollution, the burning kills the useful micro flora of the soil causing soil degradation. There is no field survey (Girdawari) data available with the Government for the areas where stubble burning is taking place. The present paper describes the methodology and results of wheat and rice residue burning areas for three districts of Haryana namely Kaithal, Kurukshetra and Karnal for the year 2010 using complete enumeration approach of multi-date IRS-P6 AWiFS and LISS-III data. In season ground truth was collected using hand held GPS and used to identify area of burnt wheat/rice residues, associated crops and land features. After geo-referencing the satellite images, district images were masked-out and multi-date image data stacks were created. Normalized Difference Vegetation Index (NDVI) of each date was generated and used at the time of classification along with other spectral bands. The non-agricultural classes in the image included: forest, wasteland, water bodies, urban/settlement and permanent vegetation etc. The vector of these non-agriculture classes were extracted from the land use, imported and mask was generated. During the classification non-agriculture area was excluded by using mask of these classes. From this the agricultural area could be separated out. The area was estimated by computing pixels under the classified image mask. In season multi-date AWiFS data along with available single-date LISS-III data between third week of April to last week of May are found to be useful for estimation of wheat residue burning areas estimation. The data between second week of October to last week of November is useful for estimation of rice residue burning areas estimation at district level.     

Class Separability Analysis and Classifier Comparison using Quad-polarization Radar Imagery

The potential of quad polarization radar data for the target discrimination has been analyzed. Quad polarization data of the RADARSAT-2 fine resolution mode has been utilized. Class separability analysis has been carried out on different polarization combinations using Transformed Divergence (TD) method and it is observed that HH-HV/VH-VV polarization combination gives better class separability when compared to other polarization combinations. Classification has been carried out on the optimized polarization combination using Maximum likelihood (MLC) and Support Vector Machine (SVM) classifiers. It is observed that SVM classification gives better classification accuracy compared to MLC. Overall classification accuracy is 93.03% for SVM and 88.78% for MLC. Class separability and classification accuracy comparison results are presented.     

3-Decadal Changes in Sea Ice Melting and Freezing Pattern in Antarctica Using SSM/I Data

The present study aims to estimate the long term changes in sea ice areal extent (SIAE) around Antarctica using Special Sensor Microwave Imager (SSM/I) satellite data (1988–2011). Daily, weekly and monthly brightness temperature (T_B) maps of Antarctica have been generated. Temporal variation in monthly averaged T_B values of sea ice have been analyzed and an increasing trend is observed in T_B. Sea ice concentration (SIC) values are estimated and the concentration class 0.85–0.95 is observed as the main contributor in sea ice areal extent during the Antarctic peak winter month i.e. September and October. SIC changes rapidly from October onwards and shows fast melting of sea ice, however, from April start of freezing has been observed. Continuous increase in SIAE is observed from February to September in all the analyzed years (1988–2011) data. In this observation period maximum SIAE is observed in September 1989 and minimum in February 1993. The Moderate Resolution Imaging Spectroradiometer (MODIS) data is used for the validation of SIAE values. SSM/I and MODIS estimated SIAE values are compared (R²?=?0.98) and a relation between them is established by performing regression analysis. This relation is further used for the estimation of corrected SIAE values. The monthly average sea ice extent maps of last three decades have been analyzed and a shift in onset of freezing of sea ice has been observed. The period of frozen sea has also been observed as decreased.     

Micro pulse lidar observations of mineral dust layer in the lower troposphere over the southwest coast of Peninsular India during the Asian summer monsoon season

A large aerosol plume with optical depth exceeding 0.7 engulfs most parts of the Arabian Sea during the Asian summer monsoon season. Based on Micro Pulse Lidar observations during the June–September period of 2008 and 2009, the present study depicts, for the first time, the existence of an elevated dust layer occurring very frequently in the altitude band of 1–3.5 km over the west coast of peninsular India with relatively large values of linear depolarization ratio (δ_L). Large values of δ_L indicate the dominance of significantly non-spherical aerosols. The aerosol optical depth of this layer (0.2) is comparable to that of the entire atmospheric column during dust-free days. Back-trajectory analysis clearly shows the advection of airmass from the arid regions of Arabia and the west Arabian Sea, through the altitude region centered around 3 km. This is in contrast to the airmass below 1 km originating from the pristine Indian Ocean region which contains relatively spherical aerosols of marine origin with δ_L generally <0.05.     

The Stability of Tide Gauges in the South Pacific Determined from Multiepoch Geodetic Levelling, 1992 to 2010

Tide gauge data is important for determining global or local sea level rise with respect to a global geocentric reference frame. Data from repeated precise levelling connections between the tide gauges and a series of coastal and inland benchmarks, including Continuous GPS (CGPS) benchmarks, are used to determine the stability of tide gauges at 12 locations in the South Pacific. The method for determining this stability is based on a constant velocity model which minimises the net movement amongst a set of datum benchmarks surveyed since the installation of the tide gauges. When assessed at a 95% confidence interval, and with the exception of the Solomon Islands, none of the tide gauges were found to be in motion relative to their CGPS benchmarks. The Solomon Islands estimate is considered to be unreliable since the CGPS benchmark was recently established and has been surveyed fewer than three times. In Tonga and Cook Islands, the tide gauges were found to be disturbed or affected by survey errors whereas the Vanuatu results were affected by earthquakes.     

Black carbon and chemical characteristics of PM<Subscript>10</Subscript> and PM<Subscript>2.5</Subscript> at an urban site of North India

The concentrations of PM₁₀, PM_2.5 and their water-soluble ionic species were determined for the samples collected during January to December, 2007 at New Delhi (28.63° N, 77.18° E), India. The annual mean PM₁₀ and PM_2.5 concentrations (± standard deviation) were about 219 (± 84) and 97 (±56) μgm⁻³ respectively, about twice the prescribed Indian National Ambient Air Quality Standards values. The monthly average ratio of PM_2.5/PM₁₀ varied between 0.18 (June) and 0.86 (February) with an annual mean of ∼0.48 (±0.2), suggesting the dominance of coarser in summer and fine size particles in winter. The difference between the concentrations of PM₁₀ and PM_2.5, is deemed as the contribution of the coarse fraction (PM_10−2.5). The analyzed coarse fractions mainly composed of secondary inorganic aerosols species (16.0 μgm⁻³, 13.07%), mineral matter (12.32 μgm⁻³, 10.06%) and salt particles (4.92 μgm⁻³, 4.02%). PM_2.5 are mainly made up of undetermined fractions (39.46 μgm⁻³, 40.9%), secondary inorganic aerosols (26.15 μgm⁻³, 27.1%), salt aerosols (22.48 μgm⁻³, 23.3%) and mineral matter (8.41 μgm⁻³, 8.7%). The black carbon aerosols concentrations measured at a nearby (∼300 m) location to aerosol sampling site, registered an annual mean of ∼14 (±12) μgm⁻³, which is significantly large compared to those observed at other locations in India. The source identifications are made for the ionic species in PM₁₀ and PM_2.5. The results are discussed by way of correlations and factor analyses. The significant correlations of Cl⁻, SO₄²⁻, K⁺, Na⁺, Ca²⁺, NO₃⁻ and Mg²⁺ with PM_2.5 on one hand and Mg²⁺ with PM₁₀ on the other suggest the dominance of anthropogenic and soil origin aerosols in Delhi.     

Monitoring and evaluation of seasonal snow cover in Kashmir valley using remote sensing,GIS and ancillary data

Seasonal snow cover is a vital natural resource in the Himalaya. Monitoring of the areal extent of seasonal snow cover is important for both climatological studies as well as hydrological applications. In the present paper, snow cover monitoring was carried out to evaluate the region-wise accumulation and ablation pattern of snow cover in Pir Panjal and Shamshawari ranges of Kashmir valley. The study was carried out for the winter period between November and April of 2004–05, 2005–06 and 2006–07, using multi-temporal WiFS sensor data of IRS-1C/1D satellites. The study shows reduction in the areal extent of seasonal snow cover and rising trend of maximum temperature in three winters for the entire Kashmir valley. This has been validated with 20 years (1988–89 to 2007–08) climatic conditions prevailed in both ranges of Kashmir valley. Region-wise study shows the spatial and temporal variability in seasonal snow cover within Kashmir valley. Advance melting was observed in Banihal and Naugam/Tangdhar regions than Gurez and Machhal regions. Different geographical parameters of these regions were studied to evaluate the influence on snow cover and it was observed that altitude and position of region with respect to mountain range are the deciding factors for retaining the seasonal snow cover for longer duration. Such region-wise study of snow cover monitoring, can provide vital inputs for planning the hydropower projects, development in habitat areas, recreational and strategic planning in the region.     

Effects of galvanic distortions on magnetotelluric data: Interpretation and its correction using deep electrical data

The non-inductive galvanic disturbances due to surficial bodies, lying smaller than high frequency skin depth, cause serious interpretational errors in magnetotelluric data. These frequency independent distortions result in a quasi-static shift between the apparent resistivity curves known as static shift. Two-dimensional modelling studies, for the effects of surficial bodies on magnetotelluric interpretation, show that the transverse electric (TE) mode apparent resistivity curves are hardly affected compared to the transverse magnetic (TM) mode curves, facilitating the correction by using a curve shifting method to match low frequency asymptotes. But in the case of field data the problem is rather complicated because of the random distribution of geometry and conductivity of near surface inhomogeneities. Here we present the use of deep resistivity sounding (DRS) data to constrain MT static shift. Direct current sensitivity studies show that the behaviour of MT static shift can be estimated using DC resistivity measurements close to the MT sounding station to appreciable depths. The distorted data set is corrected using the MT response for DRS model and further subject to joint inversion with DRS data. Joint inversion leads to better estimation of MT parameters compared to the separate inversion of data sets.