Monidipa Das
Soumya K. Ghosh
ABSTRACT
This paper proposes a novel data-driven model (BESTED), based on spatial Bayesian network with incorporated exponential smoothing mechanism, for predicting precipitation time series on daily basis. In BESTED, the spatial Bayesian network helps to efficiently model the influence of spatially distributed variables. Moreover, the incorporated exponential smoothing mechanism aids in tuning the network inferred values to compensate for the unknown factors, influencing the precipitation rate. Empirical study has been carried out to predict the daily precipitation in West Bengal, India, for the year 2015. The experimental result demonstrates the superiority of the proposed BESTED model, compared to the other benchmarks and state-of-the-art techniques.
- Noel Cressie and Christopher K Wikle. 2015. Statistics for spatio-temporal data. John Wiley & Sons.Google Scholar
- Monidipa Das, Soumya Ghosh, Pramesh Gupta, VM Chowdary, R Nagaraja, and VK Dadhwal. 2017. FORWARD: A Model for FOrecasting Reservoir WAteR Dynamics using Spatial Bayesian Network (SpaBN). IEEE Transactions on Knowledge and Data Engineering 29, 4 (2017), 842--855. Google ScholarDigital Library
- Monidipa Das and Soumya K Ghosh. 2014. A probabilistic approach for weather forecast using spatio-temporal interrelationships among climate variables. In IEEE 9th International Conference on Industrial and Information Systems. IEEE, 1--6.Google Scholar
- Monidipa Das and Soumya K Ghosh. 2017. semBnet: A Semantic Bayesian Network for Multivariate Prediction of Meteorological Time Series Data. Pattern Recognition Letters 93 (2017), 192--201.Google ScholarCross Ref
- Sujit Kumar Sahu and Khandoker Shuvo Bakar. 2012. Hierarchical Bayesian autoregressive models for large space--time data with applications to ozone concentration modelling. Applied Stochastic Models in Business and Industry 28, 5 (2012), 395--415. Google ScholarDigital Library
Index Terms
- BESTED: An Exponentially Smoothed Spatial Bayesian Analysis Model for Spatio-temporal Prediction of Daily Precipitation
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