ABSTRACT
This paper addresses geospatial interpolation for meteorological measurements in which we estimate the values of climatic metrics at unsampled sites with existing observations. Providing climatological and meteorological conditions covering a large region is potentially useful in many applications, such as smart grid. However, existing research works on interpolation either cause a large number of complex calculations or are lack of high accuracy. We propose a Bayesian compressed sensing based non-parametric statistical model to efficiently perform the spatial interpolation task. Student-t priors are employed to model the sparsity of unknown signals' coefficients, and the Approximated Variational Inference (AVI) method is provided for effective and fast learning. The presented model has been deployed at IBM, targeting for aiding the intelligent management of smart grid. The evaluations on two real world datasets demonstrate that our algorithm achieves state-of-the-art performance in both effectiveness and efficiency.
Supplemental Material
- O. Antonić, J. Krizǎn, A. Marki, and D. Bukovec. Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks. Ecological Modelling, 138:255--263, 2001.Google ScholarCross Ref
- M. Arshad, S. M. Islam, and A. Khaliq. Power transformer aging and life extension. In IEEE Conference on Probabilistic Methods Applied to Power Systems, pages 498--501, 2004.Google Scholar
- S. Babacan, R. Molina, and A. Katsaggelos. Bayesian compressive sensing using laplace priors. IEEE Transactions on Image Processing, 19(1):53--63, 2010. Google ScholarDigital Library
- A. Corduneanu and C. M. Bishop. Variational bayesian model selection for mixture distributions. In Artificial Intelligence and Statistics, pages 27--34, 2001.Google Scholar
- U. S. Department of Energy. Smart grid, 2012.Google Scholar
- D. L. Donoho. Compressed sensing. IEEE Transactions on Information Theory, 52(4):1289--1306, 2006. Google ScholarDigital Library
- M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk. Single pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 25:83--91, 2008.Google ScholarCross Ref
- N. Farouk, L. Sheng, and Q. Hayat. Effect of ambient temperature on the performance of gas turbines power plant. International Journal of Computer Science Issues (IJCSI), 10(1), 2013.Google Scholar
- R. Furrer, D. Nychka, and S. Sain. fields, 2013.Google Scholar
- W. R. Gilks, N. G. Best, and K. K. C. Tan. Adaptive rejection metropolis sampling within gibbs sampling. Journal of the Royal Statistical Society, 44(4):455--472, 1995.Google Scholar
- R. C. Gonzalez and R. E. Woods. Digital Image Processing (2nd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 2002. Google ScholarDigital Library
- L. Hernández, C. Baladrón, J. M. Aguiar, L. Calavia, B. Carro, A. Sánchez-Esguevillas, D. J. Cook, D. Chinarro, and J. Gómez. A study of the relationship between weather variables and electric power demand inside a smart grid /smart world framework. Sensors, 12(9):11571--11591, 2012.Google ScholarCross Ref
- J. R. Hipp, D. J. Bauer, P. Curran, and K. A. Bollen. Crimes of opportunity or crimes of emotion testing two explanations of seasonal change in crime. Social Forces, 82:1333--1372, 2004.Google ScholarCross Ref
- S. J. Jeffrey, J. O. Carter, K. B. Moodie, and A. R. Beswick. Using spatial interpolation to construct a comprehensive archive of australian climate data. Environmental Modelling & Software, 16(4):309--330, 2001.Google ScholarCross Ref
- S. Ji, Y. Xue, and L. Carin. Bayesian compressive sensing. IEEE Transactions on Signal Processing, 56(6):2346--2356, 2008. Google ScholarDigital Library
- KNMI. www.knmi.nlclimatologydaily_data.Google Scholar
- M. Knotters, D. Brus, and J. O. Voshaar. A comparison of kriging, co-kriging and kriging combined with regression for spatial interpolation of horizon depth with censored observations. Geoderma, 67(3-4):227--246, 1995.Google ScholarCross Ref
- M. Lustig, D. Donoho, and J. Pauly. Sparse MRI: The application of compressed sensing for rapid MR imaging. Society of Magnetic Resonance in Medicine, 58(6):1182--1195, 2007.Google ScholarCross Ref
- A. V. Oppenheim, A. S. Willsky, and S. H. Nawab. Signals and systems (2nd ed.). Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1996. Google ScholarDigital Library
- R. B. Primacka, H. Higuchib, and A. J. Miller-Rushing. The impact of climate change on cherry trees and other species in japan. Biological Conservation, 142:1943--1949, 2009.Google ScholarCross Ref
- P. F. D. Roosevelt. National weather service. Disasters, Accidents, and Crises in American History: A Reference Guide to the Nation's Most Catastrophic Events, page 1969, 2008.Google Scholar
- O. Roustant, D. Ginsbourger, and Y. Deville. Dicekriging, 2013.Google Scholar
- A. Tait, R. Henderson, R. Turner, and X. Zheng. Thin plate smoothing spline interpolation of daily rainfall for new zealand using a climatological rainfall surface. International Journal of Climatology, 26(14):2097--2115, 2006.Google ScholarCross Ref
- G. Tan, R. Shibasaki, and K. S. Rajan. The study of global land suitability evaluation: A case of potential productivity estimation for wheat. International Archives of Photogrammetry and Remote Sensing, 33:1045--1050, 2000.Google Scholar
- P. J. Tavner, D. M. Greenwood, M. W. G. Whittle, R. Gindele, S. Faulstich, and B. Hahn. Study of weather and location effects on wind turbine failure rates. Wind Energy, 16(2):175--187, 2013.Google ScholarCross Ref
- M. E. Tipping. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1:211--244, 2001. Google ScholarDigital Library
- L. A. Treinish, A. P. Praino, and Z. D. Christidis. Implementation of mesoscale numerical weather prediction for weather-sensitive business operations. In 19th International Conference on IIPS, 2003.Google Scholar
- R. Walling and G. B. Shattuck. Distribution transformer thermal behavior and aging in local-delivery distribution systems. In Proceedings of the 19th Conference on Electricity Distribution, Vienna, 2007.Google Scholar
- B. Wang and J. Xiong. Appendix: Novel geospatial inter- polation analytics for general meteorological measurements. http://filebox.vt.edu/users/claren89/Publications/2014/kdd2014_more.pdf, 2014. Google ScholarDigital Library
- Y. Yan, Y. Qian, H. Sharif, and D. Tipper. A survey on smart grid communication infrastructures: Motivations, requirements and challenges. Communications Surveys & Tutorials, IEEE, 15(1):5--20, 2013.Google ScholarCross Ref
- W. Zhang, X. Li, and R. Rutenbar. Virtual probe solver, 2010.Google Scholar
Index Terms
- Novel geospatial interpolation analytics for general meteorological measurements
Recommendations
Mean-field variational approximate Bayesian inference for latent variable models
The ill-posed nature of missing variable models offers a challenging testing ground for new computational techniques. This is the case for the mean-field variational Bayesian inference. The behavior of this approach in the setting of the Bayesian probit ...
A General Algorithm for Approximate Inference in Multiply Sectioned Bayesian Networks
IDA '01: Proceedings of the 4th International Conference on Advances in Intelligent Data AnalysisMultiply Sectioned Bayesian Networks(MSBNs) extend the junction tree based inference algorithms into a coherent framework for flexible modelling and effective inference in large domains. However, these junction tree based algorithms are limited by the ...
MODIS-based vegetation growth of temperate grassland and its correlation with meteorological factors in northern China
Fourth International Symposium on Recent Advances in Quantitative Remote SensingVegetation dynamics, particularly vegetation growth, are often used as indicators of potential grassland degradation. Grassland vegetation growth can be monitored using remotely sensed data, which has rapid and broad coverage. Grassland ecosystems are ...
Comments