Theoretical Background - Response of Flood Events to Land Use and Climate Change - page 17

Table 2.1 (continued)

Method Principle Advantages Disadvantages Best suited scenario Schematic

Inverse distance weighting (IDW) Linear combination of

known points,
weighted inversely by
distance

Ease to use, and works well with noisy
data

Spatial arrangement of samples
does not affect weights

Moderately dense
sampling with
regard to local
variation

Splines Fits a smooth curve to a

series of data points

Visually appealing curves or contour
lines

May mask uncertainty present
in the data

Irregularly spaced data

Radial basis functions (RBF) Similar to the principle of

splines, except the
curve is not smooth

Required fewer samples Required good coverage of

input space, and not suited
for extrapolation

Regions with well
distributed data
points, though
sparse

Artificial neural networks (ANN) Learn complex patterns in

the available data
using Sigmoid
functions etc.

Ability to learn and generalize data;
works well with sparse data
distributions

Risk of poor interpolation
caused by over-learning or
under- learning

Regions ranging from
sparse irregularly
distributed data to
well distributed
data

Kriging Similar to the principle of

IDW; however,
additionally accounts
for the spatial
arrangement

Best linear unbiased spatial predictor:
and no edge effects resulting from
trying to force a polynomial to fit
the data

Sophisticated programming
required; and problems of
non stationary in real
world data set

Well-distributed data
with no
discontinues

Topo to raster Not specified Based on iterative finite difference

methods. Interpolates a
hydrologically ''correct'' grid from
a set of point, line and polygon
data

Requires contour vector data as
input. Available in
ArcGIS based on
Hutchinson's ANUDEM
program

well-distributed data
points