Analysis Of Backscatter To Extraction Of Shoreline Using Machine Learning Methods In The Bangkalan Regency

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Abstract

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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Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

Jianlong Zhou, Amir H. Gandomi, Fang. Chen … (2021)

The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in medical diagnosis, financial decision-making, and in other high-stake domains. Therefore, the issue of ML explanation has experienced a surge in interest from the research community to application domains. While numerous explanation methods have been explored, there is a need for evaluations to quantify the quality of explanation methods to determine whether and to what extent the offered explainability achieves the defined objective, and compare available explanation methods and suggest the best explanation from the comparison for a specific task. This survey paper presents a comprehensive overview of methods proposed in the current literature for the evaluation of ML explanations. We identify properties of explainability from the review of definitions of explainability. The identified properties of explainability are used as objectives that evaluation metrics should achieve. The survey found that the quantitative metrics for both model-based and example-based explanations are primarily used to evaluate the parsimony/simplicity of interpretability, while the quantitative metrics for attribution-based explanations are primarily used to evaluate the soundness of fidelity of explainability. The survey also demonstrated that subjective measures, such as trust and confidence, have been embraced as the focal point for the human-centered evaluation of explainable systems. The paper concludes that the evaluation of ML explanations is a multidisciplinary research topic. It is also not possible to define an implementation of evaluation metrics, which can be applied to all explanation methods.

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Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine

Adugna Mullissa, Andreas Vollrath, Christelle Odongo-Braun … (2021)

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.

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Soil erosion and sediment dynamics in the Anthropocene: a review of human impacts during a period of rapid global environmental change

Philip N. Owens (2020)

Purpose Humanity has been modifying the planet in a measurable way for thousands of years. Recently, this influence has been such that some feel we are in a new geological epoch, the Anthropocene. This review will describe how soil erosion and sediment dynamics have (i) been used to assess the impact of humans on the planet and (ii) affected the global climate and influenced water security. Emphasis is placed on changes since the middle of the twentieth century, as this coincides with what many suggest is the start of the Anthropocene Epoch. Results and discussion The use of sediment archives has been instrumental in our understanding of how environmental systems have developed over time, both naturally and in response to anthropogenic activities. Additional information has come from measurement and monitoring programs, and tracing and fingerprinting studies. In turn, models have been developed that enable forecasting. Some of the main global impacts of enhanced soil erosion and changes in sediment dynamics and sediment composition include: changes in radiative energy balances and impacts on the cryosphere; the global carbon cycle; and greenhouse gas emissions. Impacts on water security include: effects on freshwater biota, including wild salmon populations; fluxes of contaminants, including microplastics; and reservoir and river channel sedimentation, including flooding. Sediment archives and monitoring programs have also been used to document the effect of mitigation measures and environmental policies. Conclusion Sediment archives enable us to assemble information over a variety of timescales (i.e., 10 0 to 10 5 years and longer) and a range of spatial scales (from sub-watershed to continental), in addition to environments ranging from arid to tropical to polar. Often the temporal resolution is better than other paleoenvironmental reconstruction approaches. As such, sedimentary records, when combined with measurement and monitoring approaches and other sources of information, have enabled us to determine changes in atmospheric, terrestrial, and aquatic systems, especially over the last 100 years. While soil erosion and sediment dynamics have provided a wealth of information and greatly enhanced our understanding of the role of humanity in modifying the planet, suggestions are given for further research.

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Author and article information

Journal

Title: BIO Web of Conferences

Abbreviated Title: BIO Web Conf.

Publisher: EDP Sciences

ISSN (Electronic): 2117-4458

Publication date Created: 2025

Publication date (Electronic): February 05 2025

Publication date (Print): 2025

Volume: 157

Page: 07007

Article

DOI: 10.1051/bioconf/202515707007

SO-VID: d125bef7-0bda-45de-a62c-2ca30915a89a

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https://creativecommons.org/licenses/by/4.0/

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Analysis Of Backscatter To Extraction Of Shoreline Using Machine Learning Methods In The Bangkalan Regency

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EDP Sciences: UN SDGs research collection

Most cited references 44

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine

Soil erosion and sediment dynamics in the Anthropocene: a review of human impacts during a period of rapid global environmental change

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