Abstract
In this paper, we applied the support vector machine (SVM) to the spatial interpolation of the multi-year average annual precipitation in the Three Gorges Region basin. By combining it with the inverse distance weighting and ordinary kriging method, we constructed the SVM residual inverse distance weighting, as well as the SVM residual kriging precipitation interpolation model and compared them with the inverse distance weighting, ordinary kriging, linear regression residual inverse distance weighting and linear regression residual kriging interpolation methods. The TRMM 3B43 V7 satellite precipitation information, which is processed by the latest revision algorithm, is used as the auxiliary variable for ground site precipitation interpolation along with latitude and elevation. Our results show that: (1) adding the TRMM 3B43 V7 satellite precipitation data as an auxiliary variable significantly improves the interpolation accuracy of the linear regression equation and SVM model; (2) the support vector machine hybrid interpolation method obtains superior interpolation results compared to the inverse distance weighting method, ordinary kriging method and linear regression hybrid interpolation method; (3) the interpolation accuracy of the SVM hybrid interpolation method depends on the SVM fitting degree, so we should choose a suitable fitting accuracy rather than the highest fitting accuracy; (4) the linear regression equation has a greater degree of dependency on the TRMM data than the SVM. The SVM accepts the TRMM data information while better maintaining its independence, taking into account that the TRMM data linear regression and linear regression hybrid interpolation method are not suitable for TRMM data evaluation.
Highlights
Rainfall is the most active factor in the water cycle of basins
The commonly-used precipitation interpolation methods can be divided into two categories: global interpolation methods and local interpolation methods [2,3]
This study focused on the Three Gorges Region basin between the Cuntan hydrological station along the main stream of the Yangtze River and the Wulong hydrological station along the Yangtze
Summary
Rainfall is the most active factor in the water cycle of basins. It plays an essential role in the formation of runoff. Research on the spatial interpolation of rainfall can facilitate the acquisition of the spatial distribution characteristics of rainfall, which have great significance for the analysis of basin water status, water resources management, drought and flood disaster prediction and hydrological ecological simulation [1]. The commonly-used precipitation interpolation methods can be divided into two categories: global interpolation methods (e.g., trend surface method and multiple regression method) and local interpolation methods (e.g., inverse distance weighting method and kriging method) [2,3]. The difference is whether the method uses all the site precipitation data in the study area or only the site precipitation data in the local area of the study area to predict the unknown sample. The global interpolation method and local interpolation method are combined to form a hybrid interpolation method
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
