دورية أكاديمية
A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning
| العنوان: | A Prediction Model of Marine Geomagnetic Diurnal Variation Using Machine Learning |
|---|---|
| المؤلفون: | Pan Xiong, Gang Bian, Qiang Liu, Shaohua Jin, Xiaodong Yin |
| المصدر: | Applied Sciences, Vol 14, Iss 11, p 4369 (2024) |
| بيانات النشر: | MDPI AG, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | geomagnetic diurnal variation, SVM-RF fusion model, diurnal variation law, extreme value adjustment method, pelagic geomagnetic diurnal variation, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | Geomagnetic diurnal variation significantly influences the precision of marine magnetic measurements. Precise estimation of this variation is crucial for enhancing the accuracy of offshore magnetic surveys. To address the challenges in achieving the desired accuracy with current estimation methods for geomagnetic diurnal variation, this study introduces a high-precision estimation model that integrates support vector machine (SVM) and random forest (RF) techniques. Initially, the data preprocessing phase includes an innovative extreme value adjustment method to rectify the temporal discrepancies across different stations, alongside employing the base period technique for daily baseline correction. Subsequently, we construct models to capture the daily variation trends at various times, facilitating an in-depth analysis of the diurnal variation patterns. The culmination of this process involves employing a fusion model algorithm to compute the diurnal variations across all stations comprehensively. Comparative analyses with conventional methods, such as distance weighting, bifactor weighting, and latitude weighting, reveal that our proposed model achieves a significant reduction in the root mean square error (RMSE) by an average of 31%, decreases the mean absolute error (MAE) by 35%, and enhances the Pearson correlation coefficient by 20% on average. These improvements underscore the superior accuracy of our geomagnetic diurnal variation estimation model. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2076-3417 |
| Relation: | https://www.mdpi.com/2076-3417/14/11/4369; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app14114369 |
| URL الوصول: | https://doaj.org/article/7e192120bb964e42904194b563ebd9af |
| رقم الأكسشن: | edsdoj.7e192120bb964e42904194b563ebd9af |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20763417 |
|---|---|
| DOI: | 10.3390/app14114369 |