Abstract
Bayesian networks (BNs) are one of the key computational models in traditional AI and machine learning paradigm. These are also considered to belong to the probabilistic reasoning family of computational intelligence that forms the soft part of modern AI. In this chapter, we provide a preliminary idea on standard/classical Bayesian network, followed by its parameter learning and inference generation mechanism . We also cover the basic concepts of various categories of Bayesian networks , including dynamic Bayesian network, fuzzy Bayesian network, spatial Bayesian network, semantic Bayesian network etc. Further, we discuss on the potentials of BN in modeling the inter-variable dependencies while analyzing spatio-temporal data .
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
References
Bates, B., Kundzewicz, Z., Wu, S.: Climate change and water. Intergovernmental Panel on Climate Change Secretariat (2008)
Borgelt, C., Steinbrecher, M., Kruse, R.R.: Graphical Models: Representations for Learning, Reasoning and Data Mining. Wiley, New York (2009)
Butz, C.J., Yao, H., Hua, S.: A join tree probability propagation architecture for semantic modeling. J. Intell. Inf. Syst. 33(2), 145–178 (2009)
Cano, R., Sordo, C., Gutiérrez, J.M.: Applications of Bayesian networks in meteorology. In: Advances in Bayesian Networks, pp. 309–328. Springer, Berlin (2004)
Chamoglou, M., Papadimitriou, T., Kagalou, I.: Key-descriptors for the functioning of a Mediterranean reservoir: the case of the New lake Karla-Greece. Environ. Process. 1(2), 127–135 (2014)
Christensen, N.S., Wood, A.W., Voisin, N., Lettenmaier, D.P., Palmer, R.N.: The effects of climate change on the hydrology and water resources of the Colorado River basin. Clim. Change 62(1–3), 337–363 (2004)
D’Angelo, M.F., Palhares, R.M., Cosme, L.B., Aguiar, L.A., Fonseca, F.S., Caminhas, W.M.: Fault detection in dynamic systems by a Fuzzy/Bayesian network formulation. Appl. Soft Comput. 21, 647–653 (2014)
Das, M., Ghosh, S.K.: BESTED: an exponentially smoothed spatial Bayesian analysis model for spatio-temporal prediction of daily precipitation. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 55. ACM (2017)
Das, M., Ghosh, S.K.: semBnet: a semantic Bayesian network for multivariate prediction of meteorological time series data. Pattern Recognit. Lett. 93, 192–201 (2017)
Das, M., Ghosh, S.K.: Spatio-temporal prediction of meteorological time series data: an approach based on spatial Bayesian network (SpaBN). In: International Conference on Pattern Recognition and Machine Intelligence, pp. 615–622. Springer (2017)
Das, M., Ghosh, S.K.: Spatio-temporal prediction under scarcity of influencing variables: a hybrid probabilistic graph-based approach. In: 2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), pp. 1–6. IEEE (2017)
Das, M., Ghosh, S.K.: Data-driven approaches for meteorological time series prediction: a comparative study of the state-of-the-art computational intelligence techniques. Pattern Recognit. Lett. 105, 155–164 (2018)
Das, M., Ghosh, S.K.: FB-STEP: a fuzzy bayesian network based data-driven framework for spatio-temporal prediction of climatological time series data. Expert Syst. Appl. 117, 211–227 (2019)
Das, M., Ghosh, S.K., Gupta, P., Chowdary, V.M., Nagaraja, R., Dadhwal, V.K.: FORWARD: a model for forecasting reservoir water dynamics using spatial Bayesian network (SpaBN). IEEE Trans. Knowl. Data Eng. 29(4), 842–855 (2017)
Ersel, D., İçen, D.: Fuzzy probability calculation with confidence intervals in Bayesian networks. Soft Comput. 20(2), 819–829 (2016)
Ferreira, L., Borenstein, D.: A fuzzy-Bayesian model for supplier selection. Expert Syst. Appl. 39(9), 7834–7844 (2012)
Liebig, T., Körner, C., May, M.: Fast visual trajectory analysis using spatial Bayesian networks. In: 2009 IEEE International Conference on Data Mining Workshops, pp. 668–673. IEEE (2009)
Madsen, A.L., Butz, C.J.: Exploiting semantics in Bayesian network inference using lazy propagation. In: Canadian Conference on Artificial Intelligence, pp. 3–15. Springer (2015)
McNider, R.T., Handyside, C., Doty, K., Ellenburg, W.L., Cruise, J.F., Christy, J.R., Moss, D., Sharda, V., Hoogenboom, G., Caldwell, P.: An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands. Environ. Model. Softw. 72, 341–355 (2015)
Panagopoulos, Y., Georgiou, E., Grammatikogiannis, A., Polizoi, E., Mimikou, M.: Impacts of human interaction on the sediment transport processes in the Arachtos River Basin, Western Greece. Eur. Water 21(22), 3–16 (2008)
Penz, C.A., Flesch, C.A., Nassar, S.M., Flesch, R.C., De Oliveira, M.A.: Fuzzy-Bayesian network for refrigeration compressor performance prediction and test time reduction. Expert Syst. Appl. 39(4), 4268–4273 (2012)
Piao, S., Ciais, P., Huang, Y., Shen, Z., Peng, S., Li, J., Zhou, L., Liu, H., Ma, Y., Ding, Y., et al.: The impacts of climate change on water resources and agriculture in China. Nature 467(7311), 43 (2010)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia (2016)
Tang, H., Liu, S.: Basic theory of fuzzy Bayesian networks and its application in machinery fault diagnosis. In: Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007), vol. 4, pp. 132–137. IEEE (2007)
Walker, A.R., Pham, B., Moody, M.: Spatial Bayesian learning algorithms for geographic information retrieval. In: Proceedings of the 13th annual ACM international workshop on Geographic information systems, pp. 105–114. ACM (2005)
Zhou, C., Chen, H., Peng, Z., Ni, Y., Xie, G.: A semantic Bayesian network for web mashup network construction. In: 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, pp. 645–652. IEEE (2010)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Das, M., Ghosh, S.K. (2020). Standard Bayesian Network Models for Spatial Time Series Prediction. In: Enhanced Bayesian Network Models for Spatial Time Series Prediction. Studies in Computational Intelligence, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-27749-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-27749-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27748-2
Online ISBN: 978-3-030-27749-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)