Bayesian Network with Residual Correction Mechanism

  • Monidipa DasEmail author
  • Soumya K. Ghosh
Part of the Studies in Computational Intelligence book series (SCI, volume 858)


A major issue in spatio-temporal (ST) prediction of any variable is the unavailability of the data on influencing factors. This happens, because it is not always known properly which variable influences which other. In that case, modeling of spatio-temporal inter-relationships using graphical model (like Bayesian network) becomes a challenging task due to the lack of appropriate influencing nodes in the dependency graph . In this chapter, we introduce a novel architecture of BN analysis with incorporated residual-correction mechanism  (BNRC). The embedded residual-correction mechanism in BNRC helps to compensate for the unavailable variables in the causal dependency graph  of Bayesian network , and thereby assists in improving the accuracy when adopted in a prediction model. The performance of BNRC has been evaluated in comparison with a number of conventional statistical and state-of-the-art space-time prediction models, with respect to case studies on climatological and hydrological time series prediction . Experimental result demonstrates effectiveness of BNRC in spatial time series prediction under the paucity of influencing variables.


  1. 1.
    Atkinson, K.E.: An Introduction to Numerical Analysis. Wiley (2008)Google Scholar
  2. 2.
    Cressie, N., Wikle, C.K.: Statistics for Spatio-Temporal Data. Wiley (2015)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    Das, M., Ghosh, S.K., Chowdary, V., Saikrishnaveni, A., Sharma, R.: A probabilistic nonlinear model for forecasting daily water level in reservoir. Water Resour. Manag. 30(9), 3107–3122 (2016)CrossRefGoogle Scholar
  8. 8.
    Fu, L., Qi, J.: A residual correction method for iterative reconstruction with inaccurate system model. In: 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1311–1314. IEEE (2008)Google Scholar
  9. 9.
    Galán, C.O., Matías, J.M., Rivas, T., Bastante, F.: Reforestation planning using Bayesian networks. Environ. Model. Softw. 24(11), 1285–1292 (2009)CrossRefGoogle Scholar
  10. 10.
    Partal, T., Cigizoglu, H.K., Kahya, E.: Daily precipitation predictions using three different wavelet neural network algorithms by meteorological data. Stoch. Environ. Res. Risk Assess. 29(5), 1317–1329 (2015)CrossRefGoogle Scholar
  11. 11.
    Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia (2016)zbMATHGoogle Scholar
  12. 12.
    Sahu, S.K., Bakar, K.S.: Hierarchical Bayesian autoregressive models for large space-time data with applications to ozone concentration modelling. Appl. Stoch. Models Bus. Ind. 28(5), 395–415 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Shimizu, S., Bollen, K.: Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions. J. Mach. Learn. Res. 15(1), 2629–2652 (2014)MathSciNetzbMATHGoogle Scholar
  14. 14.
  15. 15.
    R. R-3.2.2 for Windows (32/64 bit). (2016), Dec 2016
  16. 16.
    CWC. Compendium on silting of reservoirs in India. CWC (Central Water Commission) report. 2015. WS & RS Directorate, EMO, CWC. New Delhi. (2015), June 2017

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations