  There is increasing need for highly predictive and stable models for the
prediction of drought as an aid to better planning for drought response. This
paper presents the performance of both homogenous and heterogenous model
ensembles in the prediction of drought severity using the study case techniques
of artificial neural networks (ANN) and support vector regression (SVR). For
each of the homogenous and heterogenous model ensembles, the study investigates
the performance of three model ensembling approaches: linear averaging
(non-weighted), ranked weighted averaging and model stacking using artificial
neural networks. Using the approach of 'over-produce then select', the study
used 17 years of data on 16 selected variables for predictive drought
monitoring to build 244 individual ANN and SVR models from which 111 models
were selected for the building of the model ensembles. The results indicate
marginal superiority of heterogenous to homogenous model ensembles. Model
stacking is shown to realize models that are superior in performance in the
prediction of future vegetation conditions as compared to the linear averaging
and weighted averaging approaches. The best performance from the heterogenous
stacked model ensembles recorded an R2 of 0.94 in the prediction of future
vegetation conditions as compared to an R2 of 0.83 and R2 of 0.78 for both ANN
and SVR respectively in the traditional champion model approaches to the
realization of predictive models. We conclude that despite the computational
resource intensiveness of the model ensembling approach to drought prediction,
the returns in terms of model performance is worth the investment, especially
in the context of the recent exponential increase in computational power.
