semBnet: A semantic Bayesian network for multivariate prediction of meteorological time series data

Meteorological time series prediction plays a significant role in short-term and long-term decision making in various disciplines. However, it is a challenging task involving several issues. Sometimes, the available domain knowledge may help in dealing with certain issues in this regard. This work proposes a multivariate prediction approach based on a variant of semantic Bayesian network, termed as semBnet. The key objective of semBnet is to incorporate the spatial semantics as a form of domain knowledge, in standard/classical Bayesian network (SBN), and thereby improving the accuracy of meteorological prediction. It has been shown that compared to SBN, the proposed semBnet is less prone to parameter value uncertainty. Empirical studies on multivariate prediction of Temperature, Humidity, Rainfall and Soil moisture demonstrate the superiority of proposed approach over linear statistical models (e.g. ARIMA, spatio-temporal ordinary kriging (ST-OK)), and non-linear prediction techniques based on ANN, SBN, hierarchical Bayesian autoregressive model (HBAR) etc. Most significantly, compared to SBN, the proposed semBnet shows average 24% improvement in mean absolute percentage error of prediction.