A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression
Spatial interpolation of meteorological data can have immense implications on risk management and climate change planning. Kriging with external drift (KED) is a spatial interpolation variant that uses auxiliary information in the estimation of target variables at unobserved locations. However, trad...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/23/4799 |
| _version_ | 1797399865554108416 |
|---|---|
| author | Kanokrat Baisad Nawinda Chutsagulprom Sompop Moonchai |
| author_facet | Kanokrat Baisad Nawinda Chutsagulprom Sompop Moonchai |
| author_sort | Kanokrat Baisad |
| collection | DOAJ |
| description | Spatial interpolation of meteorological data can have immense implications on risk management and climate change planning. Kriging with external drift (KED) is a spatial interpolation variant that uses auxiliary information in the estimation of target variables at unobserved locations. However, traditional KED methods with linear trend functions may not be able to capture the complex and non-linear interdependence between target and auxiliary variables, which can lead to an inaccurate estimation. In this work, a novel KED method using least squares support vector regression (LSSVR) is proposed. This machine learning algorithm is employed to construct trend functions regardless of the type of variable interrelations being considered. To evaluate the efficiency of the proposed method (KED with LSSVR) relative to the traditional method (KED with a linear trend function), a systematic simulation study for estimating the monthly mean temperature and pressure in Thailand in 2017 was conducted. The KED with LSSVR is shown to have superior performance over the KED with the linear trend function. |
| first_indexed | 2024-03-09T01:47:17Z |
| format | Article |
| id | doaj.art-bfcb906bbf544bc2a5867fc1b45117cb |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T01:47:17Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-bfcb906bbf544bc2a5867fc1b45117cb2023-12-08T15:21:47ZengMDPI AGMathematics2227-73902023-11-011123479910.3390/math11234799A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector RegressionKanokrat Baisad0Nawinda Chutsagulprom1Sompop Moonchai2Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, ThailandSpatial interpolation of meteorological data can have immense implications on risk management and climate change planning. Kriging with external drift (KED) is a spatial interpolation variant that uses auxiliary information in the estimation of target variables at unobserved locations. However, traditional KED methods with linear trend functions may not be able to capture the complex and non-linear interdependence between target and auxiliary variables, which can lead to an inaccurate estimation. In this work, a novel KED method using least squares support vector regression (LSSVR) is proposed. This machine learning algorithm is employed to construct trend functions regardless of the type of variable interrelations being considered. To evaluate the efficiency of the proposed method (KED with LSSVR) relative to the traditional method (KED with a linear trend function), a systematic simulation study for estimating the monthly mean temperature and pressure in Thailand in 2017 was conducted. The KED with LSSVR is shown to have superior performance over the KED with the linear trend function.https://www.mdpi.com/2227-7390/11/23/4799geostatisticsspatial interpolationkriging with external driftleast squares support vector regressiontrend function |
| spellingShingle | Kanokrat Baisad Nawinda Chutsagulprom Sompop Moonchai A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression Mathematics geostatistics spatial interpolation kriging with external drift least squares support vector regression trend function |
| title | A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression |
| title_full | A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression |
| title_fullStr | A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression |
| title_full_unstemmed | A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression |
| title_short | A Non-Linear Trend Function for Kriging with External Drift Using Least Squares Support Vector Regression |
| title_sort | non linear trend function for kriging with external drift using least squares support vector regression |
| topic | geostatistics spatial interpolation kriging with external drift least squares support vector regression trend function |
| url | https://www.mdpi.com/2227-7390/11/23/4799 |
| work_keys_str_mv | AT kanokratbaisad anonlineartrendfunctionforkrigingwithexternaldriftusingleastsquaressupportvectorregression AT nawindachutsagulprom anonlineartrendfunctionforkrigingwithexternaldriftusingleastsquaressupportvectorregression AT sompopmoonchai anonlineartrendfunctionforkrigingwithexternaldriftusingleastsquaressupportvectorregression AT kanokratbaisad nonlineartrendfunctionforkrigingwithexternaldriftusingleastsquaressupportvectorregression AT nawindachutsagulprom nonlineartrendfunctionforkrigingwithexternaldriftusingleastsquaressupportvectorregression AT sompopmoonchai nonlineartrendfunctionforkrigingwithexternaldriftusingleastsquaressupportvectorregression |