Estimation of ice thickness on lakes using artificial neural network ensembles

Abstract

Artificial neural network ensemble (ANN ensemble) prediction is a technique in which the outputs of a set of separately trained ANNs are combined to form one unified prediction. ANN ensemble models are developed in this paper to improve the results of single artificial neural network (single ANN) for the estimation of the ice thickness in a number of selected Canadian lakes during the early winter ice growth period. An effective ensemble consists of a set of ANNs that may not be highly performing when they are used separately, but have their prediction errors greatly reduced once combined. This paper evaluates the effectiveness of a number of ensemble techniques including randomization, bagging and boosting for creating members of an ensemble, then averaging and stacking techniques for combining ensemble members. The experiments show that, in the context of estimation of lake ice thickness, boosting is much better than randomization, and sometimes better than bagging. Stacking was found to be more competitive than averaging. Overall, ANN ensemble models for the estimation of ice thickness proved to be more accurate than single ANN models, especially when boosting is used for combining ensemble members and when stacking is used to combine the outputs from individual members. ANN ensembles achieve the best generalization performance when the ensemble size is increased to around 20.