The flood prone areas maps have been

The flood-prone areas maps have been derived applying a technique of pattern recognition through computational learning. Starting from morphological attributes of a basin, embodied in the Geomorphic Flood Index, a linear binary classifier was built and used to delineate flood-prone areas according to the procedure proposed by Manfreda et al. [2–4] and Samela et al. [7,8]. To extract the fluvial geomorphology, the DEMs of HydroSHEDS (Hydrological Data and Maps Based on Shuttle Elevation Derivatives at Multiple Scales) developed by the Conservation Science Program of the World Wildlife Fund have been used. Specifically, the DEM-Void and DEM-CON with a spatial resolution of 3 arc-second have been used; the area was then regridded with a cell size of 90m. Flow direction and flow accumulation patterns were derived from the 90-m resolution DEM-CON to obtain a spatially uniform stream network for the continental U.S. Then, the procedure proposed by Giannoni et al. [1] for the identification of the drainage network from geomorphology has been carried out. It relies on a combination of contributing area and slope as a criterion for drainage network extraction. Considering the significant extent of the study area, the analyses were performed sub-dividing the Country according to the 18 water-resources regions identified in the continental U.S. by USGS (http://water.usgs.gov/GIS/huc_name.html). These geographic areas contain either the drainage area of a major river or the combined drainage areas of a series of rivers, and are depicted in Fig. 1, panel 1. For each major region, the GFI binary classifier have been trained using the 1-percent-annual-chance flood event (return cisapride cost of T=100 year) of the Federal Emergency Management Agency׳s (FEMA) Flood Insurance Rate Maps (FIRMs) [5]. The mentioned dataset has limitations, as shown in Fig. 1, panel 2, since FEMA maps contain several gaps in different zones of the U.S. where there are possible but undetermined flood hazards, since no analysis (neither with detailed or approximate methods) has been conducted. To carry out the binary classification, the standard flood hazard maps have been converted into binary maps, where the value 0 represents the areas not prone to floods (Area of Minimal Flood hazard) and the value 1 represents the 100 year floodplains. After being calibrated in a region, each GFI classifier has been applied within that water resources region. The obtained geomorphologic flood-prone areas for the continental U.S. are shown in Fig. 1, Panel 3.
Data This article describes the implementation of the photonic crystal based sensor with circular cladding with circular core. Investigation has done both for circular and elliptical holes. Table 1 is illustrating the dataset for PML depth on fiber properties; Table 2 is describing the variation of ±1% to ±2 around the optimum structure; Table 3 is describing the behavior of the PCF for different individual index based alcohol all are enumerated at the controlling wavelength λ=1.55µm and Table 4 shows the Sellmeier coefficient for silica as the PCF forming material.
Design, materials and methods Fig. 1(a) is representing the schematic end face view of the proposed fiber [1]. The innermost part of the fiber is core, represented by Fig. 1(b) and (c) with circular air hole and elliptical air hole respectively. This region is holding two layers circular hole with same diameter. Nevertheless this region is also employed elliptical air holes for study the sensitivity performance and other modal parameters for the fiber. Outer region of the core of the fiber is cladding. The diameter of the hole is denoted by d1=d5=0.90∧1, d2=d4=0.86∧1, d3=0.82∧1.The fiber has containing finite number of air holes all playing as a role of dielectric medium in this outermost cladding region. The fiber creates symmetry for this fiber. Beside this and anisotropic circular perfectly matched layer is employed here to subdue unwanted incident electromagnetic ray acts as the boundary limit condition. The PML 10% PML depth is opted here [2].The hosting material for this PCF is pure silica is preferred here for its optical novelty [1–4]. Fabrication is a vital issue for microstructure PCF. Different fabrication technique is used for their design flexibility reported in article [2–4].