Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China

[thumbnail of Chen et al. 2019_Estimation of monthly pan evaporation, TGRA, China-author version.pdf]
Preview
Text - Accepted Version
· Please see our End User Agreement before downloading.
| Preview

Please see our End User Agreement.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Chen, J.-L., Yang, H. orcid id iconORCID: https://orcid.org/0000-0001-9940-8273, Lv, M.-Q., Xiao, Z.-L. and Wu, S. J. (2019) Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China. Theoretical and Applied Climatology, 138. pp. 1095-1107. ISSN 1434-4483 doi: 10.1007/s00704-019-02871-3

Abstract/Summary

Pan evaporation plays a critical role in estimating water budget and modeling crop water requirements. However, it has been measured at a very limited number of meteorological stations. Estimation of pan evaporation from measured meteorological variables offers an important alternative and drawn increasing attention in the recent years. This paper investigated the performance of support vector machine (SVM) in the estimation of monthly pan evaporation using commonly measured meteorological variables in Three Gorges Reservoir Area in China. Evaluation suggested that SVM models showed remarkable performances and significantly outperformed the empirical model. The SVM model with polynomial as kernel function outperformed that with radial basis function. In the case of unavailable measurements of pan evaporation and meteorological variables to construct the SVM model, pan evaporation can be well-estimated by SVM model developed using data at other sites. The results indicated that the SVM method would be a promising alternative over the traditional approaches for estimating pan evaporation from measured meteorological variables.

Altmetric Badge

Item Type Article
URI https://reading-clone.eprints-hosting.org/id/eprint/84310
Identification Number/DOI 10.1007/s00704-019-02871-3
Refereed Yes
Divisions Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Publisher Springer
Download/View statistics View download statistics for this item

Downloads

Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Search Google Scholar