Abstract
Traditional spatial interpolation method of spatial interpolation such as geometric method and function method cannot estimate the theoretical error and cannot appreciate the interpolation precision, but statistical methods require data to meet a certain spatial distribution. This paper describes the basic principle of SVR and applies SVR theory to spatial interpolation, combines the SVR with the genetic algorithm for the selection of hyper parameters of SVR has a direct impact on the forecast performance, proposes a new spatial data interpolation algorithm based on SVR optimization by GA. The algorithm uses genetic algorithm to achieve the optimization of the hyper parameters using for SVR. In the process of evolution, this paper uses five folds cross-validation as the fitness function to achieve the training model’s best generalization ability. Spatial interpolation comparison 97 standard data sets are selected as research object. Experimental results show that, spatial data interpolation algorithm based on SVR optimization by GA are more accurate, and it has less dependence on the data itself and higher prediction accuracy, which is a new interpolation method with good prospect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Huai-en, Z., Sheng-xiang, H.: Interpolation of spatial data Research based on Kriging method. Surveying and Mapping Engineering 16(5), 5–13 (2007)
Xin, L., Guo-dong, C.: Comparison of Spatial Interpolation Methods. Earth Science 15(3), 260–265 (2000)
Kanevski, M., Parkin, R.: Environmental data mining and modeling based on machine learning algorithms and geostatistics. Environmental Modeling & Software 19, 845–855 (2004)
Vapnik, V.N.: The nature of statistical learning theory. Springer, NewYork (1995)
Xue-gong, Z.: On statistical learning theory and support vector machine. Acta Automatica Sinica 26(1), 32–42 (2000)
Hong-liang, D.: AGA parameter optimization of the SVR model and its prediction of the economic system. Statistics and Decision 22, 38–40 (2008)
Kanevski, M., Pozdnoukhov, A., Canu, S., Maignan, M.: Advanced Spatial Data Analysis and Modelling with Support Vector Machines[J]. International Journal of Fuzzy Systems 4(1), 606–616 (2002)
Ding-zhou, C.: GA-SVR-based exchange rate forecasting model and analysis, National Chi Nan University, a master’s degree thesis (2006)
Li, J., Cai, Z.: A Novel Automatic Parameters Optimization Approach Based on Differential Evolution for Support Vector Regression. In: Advances in Computation and Intelligence, pp. 510–519. Springer, Heidelberg (2008)
Zhao, L., Hui, G.: Digital Image Processing Based on Genetic Algorithm. China New Technology and New Products 2, 7–8 (2010)
Pozdnoukhov, A.: Support Vector Regression For Automated Robust Spatial Mapping Of Natural Radioactivity. Applied Gis 2005 1(2), 211–2110 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, W., Zhang, D., Wang, A. (2010). Research of Spatial Data Interpolation Algorithm Based on SVR Optimization by GA. In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2010. Communications in Computer and Information Science, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16388-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-642-16388-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16387-6
Online ISBN: 978-3-642-16388-3
eBook Packages: Computer ScienceComputer Science (R0)