The study indicates that analysis of morphometric parameters with the help of geographic information system (GIS) would prove a viable method of characterizing the hydrological response behaviour of the watershed. It is also well observed that remote sensing satellite data is emerging as the most effective, time saving and accurate technique for morphometric analysis of a basin. This technique is found relevant for the extraction of river basin and its stream networks through ASTER (DEM) in conjunction with remote sensing satellite data (Landsat etm?, 2013 and georeferenced survey of Indian toposheet, 1972). In this study, Kanhar basin a tributaries of Son River has been selected for detailed morphometric analysis. Seven sub-watersheds are also delineated within this basin to calculate the selected morphometric parameters. Morphometric parameters viz; stream order, stream length, bifurcation ratio, drainage density, stream frequency, form factor, circulatory ratio, etc., are calculated. The drainage area of the basin is 5,654 km 2 and shows sub-dendritic to dendritic drainage pattern. The stream order of the basin is mainly controlled by physiographic and lithological conditions of the area. The study area is designated as seventhorder basin with the drainage density value being as 1.72 km/km 2 . The increase in stream length ratio from lower to higher order shows that the study area has reached a mature geomorphic stage.

This study is an attempt to quantitatively test and compare novel advanced-machine learning algorithms in terms of their performance in achieving the goal of zonation of predicting flood susceptible areas in a low altitudinal range, sub-tropical floodplain environmental setting like that prevailing in the Middle Ganga Plain (MGP), India. This part of the Ganga floodplain region, which under the influence of undergoing active tectonic regime related subsidence, is the hotbed of annual flood disaster, and is one of the best natural laboratories to test the flood susceptibility models for establishing a universalization of such models in low relief highly flood prone areas. Based on highly sophisticated flood inventory archived for this region, and 12 flood conditioning factors viz. annual rainfall, soil type, stream density, distance from stream, distance from road, Topographic Wetness Index (TWI), altitude, slope aspect, slope, curvature, land use/land cover, and geomorphology, an advanced novel hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS), and three metaheuristic models-based ensembles with ANFIS namely ANFIS-GA (Genetic Algorithm), ANFIS-DE (Differential Evolution), and ANFIS-PSO (Particle Swarm Optimization), have been applied for zonation of the flood susceptible areas. The flood inventory dataset, prepared by satellite based collected flood samples, were apportioned into 70:30 classes to prepare training and validation datasets. One independent validation method, the Area-Under Receiver Operating Characteristic (AUROC) Curve, and other 11 cut-off-dependent model evaluation metrices have helped to conclude that the ANIFS-GA has outhustled other three models with highest success rate AUC = 0.922 and prediction rate AUC = 0.924. The accuracy was also found highest for ANFIS-GA during training (0.886) & validation (0.883).Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models for the purpose of establishing a benchmark model with capability of highest accuracy and sensitivity performance in the similar topographic and climatic setting taking assumption of the quality of input parameters as constant.

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The effects of accelerating voltage (0.5–5 keV) on the green cathodoluminescence (CL) of CaS:Ce3+ nanocrystalline powder phosphors is reported. An increase in the CL intensity was observed from the powders when the accelerating voltage was varied from 0.5 to 5 keV, which is a relevant property for a phosphor to be used in field emission displays (FEDs). The CL degradation induced by prolonged electron beam irradiation was analyzed using CL spectroscopy, x-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES). The AES data showed the decrease in the S peak intensity and an increase in the O peak intensity during electron bombardment. The CL intensity was found to decrease to 30% of its original intensity after about 50 C/cm2. XPS was used to study the chemical composition of the CaS:Ce3+ nanophosphor before and after degradation. The XPS data confirms that a nonluminescent CaSO4 layer has formed on the surface during the degradation process, which may partially be responsible for the CL degradation. The electron stimulated surface chemical reaction mechanism was used to explain the effects of S desorption and the formation of the nonluminescent CaSO4 layer on the surface.

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