Determining the Suitability of Two Different Statistical Techniques in Shallow Landslide (Debris Flow) Initiation Susceptibility Assessment in the Western Ghats

In the present study, the Information Value (InfoVal) and the Multiple Logistic Regression (MLR) methods based on bivariate and multivariate statistical analysis have been applied for shallow landslide initiation susceptibility assessment in a selected subwatershed in the Western Ghats, Kerala, India, to determine the suitability of geographical information systems (GIS) assisted statistical landslide susceptibility assessment methods in the data constrained regions. The different landslide conditioning terrain variables considered in the analysis are geomorphology, land use/land cover, soil thickness, slope, aspect, relative relief, plan curvature, profile curvature, drainage density, the distance from drainages, lineament density and distance from lineaments. Landslide Susceptibility Index (LSI) maps were produced by integrating the weighted themes and divided into five landslide susceptibility zones (LSZ) by correlating the LSI with general terrain conditions. The predictive performances of the models were evaluated through success and prediction rate curves. The area under success rate curves (AUC) for InfoVal and MLR generated susceptibility maps shows 84.11% and 68.65%, respectively. The prediction rate curves show good to moderate correlation between the distribution of the validation group of landslides and LSZ maps with AUC values of 0.648 and 0.826 respectively for MLR and InfoVal produced LSZ maps. Considering the best fit and suitability of the models in the study area by quantitative prediction accuracy, LSZ map produced by the InfoVal technique shows higher accuracy, i.e. 82.60%, than the MLR model and is more realistic while compared in the field and is considered as the best suited model for the assessment of landslide susceptibility in areas similar to the study area. The LSZ map produced for the area can be utilised for regional planning and assessment process, by incorporating the generalised rainfall conditions in the area

SmBa2NbO6 Nanopowders, an Effective Percolation Network Medium for YBCO Superconductors

The percolation behavior of superconductor-insulator composite, YBa2Cu3O7–δ, and nano SmBa2NbO2 synthesized by modified combustion technique was studied. Particle size of nano SmBa2NBO6 was determined using transmission electron microscopy. The chemical nonreactivity of nano SmBa2NbO6 with YBCO is evident from the X-Ray diffraction study which makes it a suitable nanoceramic substrate material for high temperature superconducting films. A systematic increase in the sintered density, approaching the optimum value of the insulating nanophase is clearly observed, as the vol.% of YBCO in the composite decreases. SEM micrograph showed uniform distribution of nanopowder among the large clusters of YBCO. The obtained percolation threshold is ~26 vol% of YBCO in the composite. All the composites below the threshold value showed TC(0)~92 K even though the room resistivity increases with increase in vol.% of nano SmBa2NbO6. The values of critical exponents obtained matches well with the theoretically expected ones for an ideal superconductor-insulator system