Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion
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 ge...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Geosciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3263/15/1/2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588440069210112 |
|---|---|
| author | Daria Bogatova Stanislav Ogorodov |
| author_facet | Daria Bogatova Stanislav Ogorodov |
| author_sort | Daria Bogatova |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-beae5edaa37241919b5aa92cf89aed6b |
| institution | Kabale University |
| issn | 2076-3263 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj-art-beae5edaa37241919b5aa92cf89aed6b2025-01-24T13:34:05ZengMDPI AGGeosciences2076-32632024-12-01151210.3390/geosciences15010002Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast ErosionDaria Bogatova0Stanislav Ogorodov1Faculty of Geography, Lomonosov Moscow State University, Moscow 119991, RussiaFaculty of Geography, Lomonosov Moscow State University, Moscow 119991, RussiaThis 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.https://www.mdpi.com/2076-3263/15/1/2arcticcoastal retreatpermafrostexogenous processesKara Seadata analysis |
| spellingShingle | Daria Bogatova Stanislav Ogorodov Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion Geosciences arctic coastal retreat permafrost exogenous processes Kara Sea data analysis |
| title | Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion |
| title_full | Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion |
| title_fullStr | Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion |
| title_full_unstemmed | Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion |
| title_short | Applying Data Analysis and Machine Learning Methods to Predict Permafrost Coast Erosion |
| title_sort | applying data analysis and machine learning methods to predict permafrost coast erosion |
| topic | arctic coastal retreat permafrost exogenous processes Kara Sea data analysis |
| url | https://www.mdpi.com/2076-3263/15/1/2 |
| work_keys_str_mv | AT dariabogatova applyingdataanalysisandmachinelearningmethodstopredictpermafrostcoasterosion AT stanislavogorodov applyingdataanalysisandmachinelearningmethodstopredictpermafrostcoasterosion |