BESTED: An Exponentially Smoothed Spatial Bayesian Analysis Model for Spatio-temporal Prediction of Daily Precipitation

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.