Model ensembles of artificial neural networks and support vector regression for improved accuracy in the prediction of vegetation conditions

TL;DR

This study compares different ensemble methods of neural networks and support vector regression to improve drought prediction accuracy, finding that heterogenous stacked ensembles outperform traditional models.

Contribution

It introduces a comprehensive evaluation of homogenous and heterogenous ensemble approaches, especially model stacking, for drought prediction using ANN and SVR.

Findings

Heterogenous ensembles outperform homogenous ones.

Model stacking yields the highest R2 of 0.94.

Ensembling improves drought prediction accuracy significantly.

Abstract

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.