Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment
GNSS technology utilizes satellite signals to determine the position of a point on Earth. Using this location information, the GNSS velocities of the points can be calculated. GNSS velocity accuracies are crucial for studies requiring high precision, as fault slip rates typically range within a few...
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MDPI AG
2024-12-01
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| author | Halil İbrahim Solak |
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| description | GNSS technology utilizes satellite signals to determine the position of a point on Earth. Using this location information, the GNSS velocities of the points can be calculated. GNSS velocity accuracies are crucial for studies requiring high precision, as fault slip rates typically range within a few millimeters per year. This study employs machine learning (ML) algorithms to predict GNSS velocity accuracies for fault slip rate estimation and earthquake hazard analysis. GNSS data from four CORS stations collected over 1-, 2-, and 3-year intervals with observation durations of 2, 4, 6, 8, and 12 h, were analyzed to generate velocity estimates. Position accuracies, observation intervals, and corresponding velocity accuracies formed two datasets for the East and North components. ML models, including Support Vector Machine, Random Forest, K-Nearest Neighbors, and Multiple Linear Regression, were used to model the relationship between position and velocity accuracies. The findings reveal that the Random Forest, which makes more accurate and reliable predictions by evaluating many decision trees together, achieved over 90% accuracy for both components. Velocity accuracies of ±1.3 mm/year were obtained for 1-year interval data, while accuracies of ±0.6 mm/year were achieved for the 2- and 3-year intervals. Three campaigns were deemed sufficient for Holocene faults with higher slip rates. However, for Quaternary faults with lower slip rates, longer observation periods or additional campaigns are necessary to ensure reliable velocity estimates. This highlights the need for GNSS observation planning based on fault activity. |
| format | Article |
| id | doaj-art-5c18801c9fa044159e94999176828345 |
| institution | Kabale University |
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| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-5c18801c9fa044159e949991768283452025-01-10T13:14:29ZengMDPI AGApplied Sciences2076-34172024-12-0115111310.3390/app15010113Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard AssessmentHalil İbrahim Solak0Distance Education Vocational School, Afyon Kocatepe University, Afyonkarahisar 03200, TürkiyeGNSS technology utilizes satellite signals to determine the position of a point on Earth. Using this location information, the GNSS velocities of the points can be calculated. GNSS velocity accuracies are crucial for studies requiring high precision, as fault slip rates typically range within a few millimeters per year. This study employs machine learning (ML) algorithms to predict GNSS velocity accuracies for fault slip rate estimation and earthquake hazard analysis. GNSS data from four CORS stations collected over 1-, 2-, and 3-year intervals with observation durations of 2, 4, 6, 8, and 12 h, were analyzed to generate velocity estimates. Position accuracies, observation intervals, and corresponding velocity accuracies formed two datasets for the East and North components. ML models, including Support Vector Machine, Random Forest, K-Nearest Neighbors, and Multiple Linear Regression, were used to model the relationship between position and velocity accuracies. The findings reveal that the Random Forest, which makes more accurate and reliable predictions by evaluating many decision trees together, achieved over 90% accuracy for both components. Velocity accuracies of ±1.3 mm/year were obtained for 1-year interval data, while accuracies of ±0.6 mm/year were achieved for the 2- and 3-year intervals. Three campaigns were deemed sufficient for Holocene faults with higher slip rates. However, for Quaternary faults with lower slip rates, longer observation periods or additional campaigns are necessary to ensure reliable velocity estimates. This highlights the need for GNSS observation planning based on fault activity.https://www.mdpi.com/2076-3417/15/1/113machine learningGNSS velocity accuracyGNSS position accuracyslip rate |
| spellingShingle | Halil İbrahim Solak Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment Applied Sciences machine learning GNSS velocity accuracy GNSS position accuracy slip rate |
| title | Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment |
| title_full | Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment |
| title_fullStr | Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment |
| title_full_unstemmed | Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment |
| title_short | Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment |
| title_sort | prediction of gnss velocity accuracies using machine learning algorithms for active fault slip rate determination and earthquake hazard assessment |
| topic | machine learning GNSS velocity accuracy GNSS position accuracy slip rate |
| url | https://www.mdpi.com/2076-3417/15/1/113 |
| work_keys_str_mv | AT halilibrahimsolak predictionofgnssvelocityaccuraciesusingmachinelearningalgorithmsforactivefaultslipratedeterminationandearthquakehazardassessment |