Saeed Talatian Azad

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.