Analysis Of Backscatter To Extraction Of Shoreline Using Machine Learning Methods In The Bangkalan Regency
Coastal areas are often threatened by natural and anthropogenic factors, causing instability and shoreline changes in the affected areas. Shoreline changes can be monitored with remote sensing techniques such as Synthetic Aperture Radar (SAR) data. The purpose of this research is to extract the coas...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2025/08/bioconf_srcm24_07007.pdf |
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| Summary: | Coastal areas are often threatened by natural and anthropogenic factors, causing instability and shoreline changes in the affected areas. Shoreline changes can be monitored with remote sensing techniques such as Synthetic Aperture Radar (SAR) data. The purpose of this research is to extract the coastline by segmenting the machine learning method and find out how far the machine learning model works to distinguish the water class and the land class. The method used in this research is the Support Vector Machine model to divide the water and land classes that will be utilized to obtain shoreline extracts from the model results, and evaluate the model by calculating the model accuracy. The overall accuracy results recorded in 2016 and 2023 are 99.5% and 99%, respectively, with Kappa Coefficients of 0.99018 and 0.98138. This study highlights the potential of SAR data and SVM methods in monitoring coastal dynamics and can serve as a reference for sustainable coastal management. |
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| ISSN: | 2117-4458 |