Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion
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| Publicado en: | Geosciences vol. 15, no. 1 (2025), p. 2 |
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| Autor principal: | |
| Otros Autores: | |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3159470463 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2076-3263 | ||
| 024 | 7 | |a 10.3390/geosciences15010002 |2 doi | |
| 035 | |a 3159470463 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 231468 |2 nlm | ||
| 100 | 1 | |a Bogatova, Daria | |
| 245 | 1 | |a Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study aims to establish a scientific and methodological basis for predicting shoreline positions using modern data analysis and machine learning techniques. The focus area is a 5 km section of the Ural coast along Baydaratskaya Bay in the Kara Sea. This region was selected due to its diverse geomorphological features, varied lithological composition, and significant presence of permafrost processes, all contributing to complex patterns of shoreline change. Applying advanced data analysis methods, including correlation and factor analysis, enables the identification of natural signs that highlight areas of active coastal retreat. These insights are valuable in arctic development planning, as they help to recognize zones at the highest risk of significant shoreline transformation. The erosion process can be conceptualized as comprising two primary components to construct a predictive model for coastal retreat. The first is a random variable that encapsulates the effects of local structural changes in the coastline alongside fluctuations due to climatic conditions. This component can be statistically characterized to define a confidence interval for natural variability. The second component represents a systematic shift, which reflects regular changes in average shoreline positions over time. This systematic component is more suited to predictive modeling. Thus, modern information processing methods allow us to move from descriptive to numerical assessments of the dynamics of coastal processes. The goal is ultimately to support responsible and sustainable development in the highly sensitive arctic region. | |
| 651 | 4 | |a Alaska | |
| 651 | 4 | |a United States--US | |
| 651 | 4 | |a Kara Sea | |
| 653 | |a Permafrost | ||
| 653 | |a Shoreline changes | ||
| 653 | |a Datasets | ||
| 653 | |a Coastal erosion | ||
| 653 | |a Sea level | ||
| 653 | |a Lithology | ||
| 653 | |a Geomorphology | ||
| 653 | |a Sediments | ||
| 653 | |a Data analysis | ||
| 653 | |a Machine learning | ||
| 653 | |a Natural variability | ||
| 653 | |a Coastal dynamics | ||
| 653 | |a Shorelines | ||
| 653 | |a Climate change | ||
| 653 | |a Prediction models | ||
| 653 | |a Coasts | ||
| 653 | |a Remote sensing | ||
| 653 | |a Arctic zone | ||
| 653 | |a Climatic conditions | ||
| 653 | |a Shoreline protection | ||
| 653 | |a Random variables | ||
| 653 | |a Sustainable development | ||
| 653 | |a Information processing | ||
| 653 | |a Data processing | ||
| 653 | |a Discriminant analysis | ||
| 653 | |a Erosion processes | ||
| 653 | |a Factor analysis | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Beaches | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Ice environments | ||
| 653 | |a Coastal processes | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Neural networks | ||
| 653 | |a Morphology | ||
| 653 | |a Coastal plains | ||
| 700 | 1 | |a Ogorodov, Stanislav | |
| 773 | 0 | |t Geosciences |g vol. 15, no. 1 (2025), p. 2 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159470463/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159470463/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159470463/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |