Results - Response of Flood Events to Land Use and Climate Change - page 158

Table 6.14 SAM statistical results for comparing estimated elevation with high resolution DEM

(5 m)

DEM 5 m Estimated Residuals

DEM 10 m before resampling
Min

1069 1120.773 -352

Max 1749 1615.079 527.936

Mean 1245.237 1245.237 \.001

S.D. 126.646 91.439 87.625

DEM 10 m after resampling
Min 1069 1073.306 -135.696

Max 1749 1756.126 157.32

Mean 1245.237 1245.237 \.001

S.D. 126.646 126.311 9.206

4. R-square is a statistical measure of how well a regression line approximates real
data points; an R-squared of ''1.0'' (100 %) indicates a perfect fit.

As a general conclusion, in terms of RMSE analysis, better results in the
accuracy of the model were obtained when a DEM was used after resampling. The
mean error in resampled DEM was 4.292, much less than the value of 25.928 for
DEM before resampling. Maximum absolute error also shows that the errors are
not so large in DEM after resampling. R-square value between the regression line
of both DEMs shows that, in terms of R-square, the results fitted well in DEM
especially after resampling procedures. Therefore, as an overall result, it could be
concluded that a low resolution DEM could show better and more accurate results
when it is resampled to a high resolution DEM.
For other additional analysis a Geostatistical based software so-called ''SAM''
was applied. It offers a comprehensive array of spatial statistical methods. The
methods available in SAM range from simple tools for exploratory graphical
analysis (e.g., mapping and graphing) and descriptive statistics of spatial patterns
(e.g., autocorrelation metrics), to advanced spatial regression models (e.g., auto-
regression and eigenvector filtering). Table 6.9 represents the SAM statistical
analysis for comparing estimated elevation with high resolution DEM (5 m).
First, the analytical outcome in ''SAM'' environment was the standard
regression output. According to the results, Standard error for DEM 10 m before
resampling is 0.007 and after resampling this value decrease to\.001. The P value
in both models is \0.001. Second, the predicted values for each method
were compared with original values of source data points. The minimum,
maximum, mean, and standard deviations for each method were calculated and the
descriptive statistical results are shown in Table 6.14 . These parameters are
defined as following:

• Minimum absolute error shows how small the errors can be in case of considering
lowest elevation in both observed and estimated value.
• Maximum absolute error shows how large the errors can be in case of considering
highest elevation in both observed and estimated value.