Using Non-authoritative Sources During Emergencies in Urban Areas - Computational Approaches for Urban Environments - page 337

different locations throughout the domain. Water height modeled for each point is
interpolated using a spline function to create a water height surface. A DEM with
a 1 arc-second resolution from the National Elevation Dataset (NED) is subtracted
from the water height surface to create a water depth layer (USGS 2013 ).

United States Census Bureau

A 2012 TIGER/line ® shapefile of road networks for the New York City area was
downloaded from the US Census Bureau and was georeferenced to New York State
Plane coordinates.

14.2.2 Damage Assessment During Emergencies

After individual data layers are generated from available remote sensing and
authoritative and non-authoritative data, they are integrated together using an
artificial neural network machine learning algorithm. Artificial neural networks are
nonlinear data modeling tools for discovering patterns in data from a series of
inputs (Atkinson and Tatnall 1997 ). The network consists of interconnected nodes
comprising an input layer, a hidden layer, and an output layer (Fig. 14.5 ). In this
research, the nodes of the input layer consist of the flood identification layers created
during preprocessing, and the output layer is a flood assessment surface. The hidden
layer nodes, or neurons, are the computational units of the network. The neuron
receives the inputs and produces responses. Benediktsson et al. ( 1990 ) defines the
simplest formal model of the neuron, where the output value is approximated by the
function

Input
layer

Hidden
layer

Output
layer

Input #1

Input #2

Output

Input #3

Input #4

Fig. 14.5 Depiction of an artificial neural network