FB-STEP: A fuzzy Bayesian network based data-driven framework for spatio-temporal prediction of climatological time series data

Abstract

With the recent development of computational intelligence (CI), data-driven models have gained growing interest to be applied in various scientific disciplines. This paper aims at proposing a hybrid CI-based data-driven framework as a complement for the physics-based models used in climatological prediction. The proposed framework, called FB-STEP, is based on a combination of fuzzy Bayesian strategy and multifractal analysis technique. The focus is to address three major research challenges in multivariate climatological prediction: (1) modeling complex spatio-temporal dependency among climatological variables, (2) dealing with non-linear, chaotic dynamics in climatic time series, and (3) reducing epistemic uncertainty in the data-driven prediction process. The present work not only explores Fuzzy-Bayesian modeling of spatio-temporal processes, but also presents an elegant approach of dealing with intrinsic chaos in time series, through a synergism between multifractal analysis and Bayesian inference mechanism. Similar concepts may also be successfully employed in developing expert or intelligent systems for wide range of applications, including reservoir-water dynamics modeling, flood monitoring, traffic flow modeling, chemical-mechanical process monitoring, and so on. Thus, the present research work carries a significant value not merely in the field of climate research, but also in the domains of AI and machine intelligence. The experimentation has been carried out to spatio-temporally extrapolate the climatic conditions of five different locations in India, with the help of historical data on temperature, humidity, precipitation rate, and soil moisture. A comparative study with popular linear and non-linear methods has validated the efficacy of the proposed data-driven approach for climatological prediction.