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Abstract
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Digital Shoreline Analysis System (DSAS)
is the most frequently used coastal engineering system
for shoreline change quantification. Factors like human
and system errors, wrong perception of the shoreline
changes, and non-exact data sources may cause
errors in the measured data. Detection and modification
of such data can increase the accuracy of results.
At present, the DSAS tool lacks this capability, so this
research aimed to present a new module for DSAS to
detect uncertain data in shoreline change rate measurements.
The module’s basis for detecting uncertain data
is to use statistical methods: adjusted boxplot, Grubbs’
test, standard deviation tests, median test, modified
Z-score test, and voting method. The module’s performance
was evaluated based on a data set obtained
through Qeshm Island shoreline change quantification
in Iran. The details of these methods, the prepared module, the case study, and the shoreline change measurement
statistical methods were discussed in this
study. The results showed the acceptable output of this
module in detecting uncertain data.
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