Digital Shoreline Analysis System improvement for uncertain data detection in measurements

Area of Science:

• Coastal geomorphology
• Geographic Information Systems (GIS)
• Statistical analysis

Background:

• The Digital Shoreline Analysis System (DSAS) is widely used for quantifying shoreline changes.
• Data errors from various sources can compromise the accuracy of DSAS measurements.
• Current DSAS versions lack integrated methods for detecting and managing uncertain data.

Purpose of the Study:

• To develop and present a novel module for DSAS.
• The module aims to detect uncertain data within shoreline change rate measurements.
• Enhance the reliability of coastal change analysis using DSAS.

Main Methods:

• Statistical methods including adjusted boxplot, Grubbs' test, standard deviation tests, median test, and modified Z-score.
• A voting method was incorporated for robust uncertainty detection.
• The module was tested using shoreline data from Qeshm Island, Iran.

Main Results:

• The developed module demonstrated acceptable performance in identifying uncertain data points.
• Statistical tests effectively flagged data points deviating from expected patterns.
• The case study validated the module's utility in real-world coastal data.

Conclusions:

• The new DSAS module successfully addresses the need for uncertain data detection.
• Implementing this module can significantly improve the accuracy of shoreline change rate calculations.
• This research contributes to more reliable coastal engineering and management practices.