The assessment of slope stability along NH-22 in Rampur-Jhakri Area,Himachal Pradesh

The present paper demonstrates the assessment of slope stability analysis between Rampur to Jhakri road section along National Highway (NH-22), Himachal Pradesh, India. The different types of slope failures have affected most part of slopes which causes considerable loss of life and property, inconveniences such as disruption of traffic along highways. The poorly designed rock slopes for road widening or construction purposes may weaken the stability of the slopes. A detail field investigation has been carried out to collect the representative rock samples for determination of physico-mechanical properties of rock and joint data for kinematic analysis. The rocks exposed in the area are highly jointed quartzite and quartz-mica schist of Rampur-Larji Group of Palaeoproterozoic age. The continuous slope mass rating (CSMR) technique has been applied for the assessment of slope stability analysis at five vulnerable locations and the results shows slopes are partially stable to unstable. Kinematic analysis mainly shows wedge type of failure along with few toppling and planar failures. The existing slope required immediate treatment to prevent the failure for its long term stability.     

A statistical significance of differences in classification accuracy of crop types using different classification algorithms

Crop classification is needed to understand the physiological and climatic requirement of different crops. Kernel-based support vector machines, maximum likelihood and normalised difference vegetation index classification schemes are attempted to evaluate their performances towards crop classification. The linear imaging self-scanning (LISS-IV) multi-spectral sensor data was evaluated for the classification of crop types such as barley, wheat, lentil, mustard, pigeon pea, linseed, corn, pea, sugarcane and other crops and non-crop such as water, sand, built up, fallow land, sparse vegetation and dense vegetation. To determine the spectral separability among crop types, the M-statistic and Jeffries–Matusita (J–M) distance methods have been utilised. The results were statistically analysed and compared using Z-test and χ²-test. Statistical analysis showed that the accuracy results using SVMs with polynomial of degrees 5 and 6 were not significantly different and found better than the other classification algorithms.     

Far infrared emission from galaxies

Until recently far infrared (FIR) observations of galaxies were limited to about a dozen bright and/or active galaxies. New photometric data has become available from Infrared Astronomical Satellite (IRAS) on 33 galaxies (most of them faint) from IRAS Circular Nos. 1, 2, and 3 The FIR spectra of these galaxies are similar. The far infrared fluxF _FIR in the wavelength interval 9–118 m of the brighter galaxies is seen to be correlated with the integrated optical magnitudeB _T ⁰ . The 12 and 25 m fluxes of these galaxies exhibit the same dependence onB _T ⁰ as the 10 and 21 m fluxes from Seyferts and other emission-line galaxies. This suggests that the galaxies detected by IRAS are some type of active galaxies in accord with the high percentage of these galaxies predicted by Lock and Rowan-Robinson (1983).     

Near infrared photometry of some RS CVn systems and candidates

Infrared observations in the J, H, and K bands were made on eleven RS CVn systems. Infrared (IR) excess 0.3 mag in the J, H, and K bands was observed from the three systems UX Ari, HR 1099 and CrB. Similar observations were also made on the RS CVn candidates HD 26354, 39937, 42449, 51268, and 86005.The IR excess in the RS CVn systems for which near infrared photometric data are available is found to be correlated with the maximum of optical wave amplitude of these systems.The observed excess cannot be explained on the basis of a single process such as: (i) emission from cool spots on the photosphere; (ii) free-free emission from an optically thin circumstellar plasma; or (iii) dust grain emission from circumstellar material.On leave from TIFR, Bombay, India.     

On the occurrence rate of high-speed solar wind events

We have investigated the rate of occurrence of solar wind phenomena observed between 1972–1984 using power-spectrum analysis. The data have been taken from the high-speed solar wind (HSSW) stream catalogue published by Mavromichalaki, Vassilaki, and Marmatsouri (1988). The power-spectrum analysis of HSSW events indicates that HSSW stream events have a periodicity of 9 days. This periodicity of HSSW events is of the 27-day period of coronal holes, which are major sources of solar wind events. In our opinion, the 9-day period may be the energy build-up time for coronal hole regions to produce the HSSW stream events.     

Performance analysis of different predictive models for crop classification across an aridic to ustic area of Indian states

The purpose of this study is to present comparative performance analysis of different machine learning algorithms for large area crop classification. Ten Indian districts with significant rabi crops viz. wheat, mustard, gram, red lentils (masoor) have been selected for the study. Most popular classical ensemble models – bagging/ARCing, random forest (RF), gradient boosting and Importance Sampled Learning Ensemble (ISLE) with traditional single model (decision tree) have been selected for comparative analysis. To incorporate dependency of large area crop in different variables viz. parent material and soil, phenology, texture, topography, soil moisture, vegetation, climate etc., 35 digital layers are prepared using different satellite data (ALOS DEM, Landsat-8, MODIS NDVI, RISAT-1, Sentinental-1A) and climatic data (precipitation, temperature). In rabi season, field survey about crop type is carried out to prepare training data. Performance is evaluated on the basis of marginal rates, F-measure and Jaccard’s coefficient of community, Classification Success Index and Agreement Coefficients. Score is calculated to rank the algorithm. RF is best performer followed by gradient boosting for crop classification. Other ensemble methods ARCing, bagging and ISLE are in decreasing order of performance. Traditional non-ensemble method decision tree scored higher than ISLE.     

Vegetation water content retrieval using scatterometer data at X-band

The vegetation water content (VWC) of kidney bean crop is retrieved using ground-based multi-temporal, multi-angular and co-polarized scatterometer data at X-band. An outdoor crop-bed was prepared to observe the scatterometer response at HH- and VV-polarizations in the angular range from 20° to 70°. The trend of scattering coefficient is found to decrease for the entire angular range. The present study is carried out to investigate the retrieval for VWC of a kidney bean crop at its nine different growth stages by an empirical relation based on least square optimization method using scatterometer data. The results are found promising for the retrieval of the VWC of kidney bean crop at its several growth stages.