Abstract
There are several approaches to handle spatial trends: Kriging, stochastic simulation models, fractal and so on. The paper presents some contemporary approaches to spatial data analysis. The main topics are concentrated on the problems of space regression analysis by geochemical exploration data modeling. The innovative part of the paper presents integrated/hybrid model-combine GEP evolution modeling with spatial structure analysis. The models are based on GEP evolution modeling algorithm. Geostatistical tools on the basis of spatial autocorrelation thesis are used to extract representative data to fully utilize spatial structural information and weaken the influence of noise. Case study from mineral deposits in Gejiu illustrates the performance of the proposed model and BP neural network model is chosen as comparative study. It is shown that the fitting of the model and precision of test, provided by the combination of GEP evolution modeling and geostatistical model based approaches, are obviously improved.
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
Ferreira, C.: Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems 13(2), 87–129 (2001)
Zhang, Z., Xia, Q.: Modeling the Spatial Patterns of Mineralization Environments Using Fuzzy Sets. Earth science 30(1), 109–113 (2005)
Li, Q.: Study on Interpolation Method of Soil Spatial Information Based on RBF Neural Networks [master’s paper]. Sichuan: Sichuan Agricultural University (2006)
Li, Q., Cai, Z.-h., Zhao, Y.: Application of Gene expression programming in prediction the Amount of Gas from Coal Face. Journal of basic science and engineering 2(1) (March 2004)
Jiang, S., Cai, Z., Li, Q.: A novel Parallel Gene Expression Programming algorithm Based on MPI. Acta Electronica Sinica 10(1) (October 2005)
Jia, X., Tang, C.J., Zuo, J.: Mining Frequent Function Set Based on Gene Expression Programming. Chinese Journal of computers 28(8), 1247–1254 (2005)
Chen, C.: Application and Research on mining Geological data based on evolution Calculation [doctoral dissertation]. Wuhan, China University of Geosciences (2006)
Kanevski, M., Parkin, R., Pozdnukhov, A., Timonin, V., Maignan, M., Demyanov, V., Canu, S.: Environmental data mining and modelling based on machine learning algorithms and geostatistics. Environmental Modelling & Software 19(9), 845–856 (2004)
Kanevski, M., Pozdnoukhov, A., Canu, S., Maignan, M.: Advanced Spatial Data Analysis and Modelling with Support Vector Machines. International Journal of Fuzzy Systems 4(1), 606–616 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, D., Wang, A., Chen, Z. (2008). Space Regression Analysis on Geochemical Data by the GEP Evolutionary Model Based on Kriging. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-92137-0_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92136-3
Online ISBN: 978-3-540-92137-0
eBook Packages: Computer ScienceComputer Science (R0)