Abstract:
This paper presented trend analysis of droughts in Kerala, Telangana, and Orissa
meteorological subdivisions in India and proposed a framework for drought prediction
by employing the Empirical Mode Decomposition (EMD)‐based prediction models.
The study used 3‐month standardized precipitation index (SPI3) for drought analysis.
The trend analysis of SPI3 series for the period 1871–2012 using Mann–Kendall
method showed statistically significant increasing trend in Kerala and Telangana sub divisions and a decreasing trend in Orissa subdivision. In addition, the non‐linear trend
component extracted from EMD showed statistically significant changes in all the
three subdivisions. Then, the study proposed a hybrid approach for prediction of
short‐term droughts by coupling multivariate extension of EMD (MEMD) with
stepwise linear regression (SLR) and genetic programming (GP) methods. First, the
multivariate dataset comprising the SPI3 series of current and lagged time steps are
decomposed using the MEMD. Then, SLR/GP models are developed to predict each
subseries of SPI3 of desired time step considering the subseries of predictor variables
at the corresponding timescales as inputs. The resulting models at different timescales
are recombined to obtain the SPI3 values of the desired time step. The method is
applied for prediction of short‐term droughts in the three subdivisions. The results
obtained by the hybrid models are compared with that obtained by conventional pre diction models using M5 Model Trees and GP. The rigorous performance evaluation
based on multiple statistical criteria clearly exhibited the superiority of the hybrid
approaches (i.e., prediction models with MEMD‐based decomposition over models
without decomposition) for prediction of SPI3 in three subdivisions. Further, the study
found that MEMD‐GP model performs marginally better than the MEMD‐SLR model
due to its efficacy in modelling high frequency modes.
