On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks

Abstract

Artificial neural networks (ANNs) become widely used for runoff forecasting in numerous studies. Usually classical gradient-based methods are applied in ANN training and a single ANN model is used. To improve the modelling performance, in some papers ensemble aggregation approaches are used whilst in others, novel training methods are proposed. In this study, the usefulness of both concepts is analysed. First, the applicability of a large number of population-based metaheuristics to ANN training for runoff forecasting is tested on data collected from four catchments, namely upper Annapolis (Nova Scotia, Canada), Biala Tarnowska (Poland), upper Allier (France) and Axe Creek (Victoria, Australia). Then, the importance of the search for novel training methods is compared with the importance of the use of a very simple ANN ensemble aggregation approach. It is shown that although some metaheuristics may slightly outperform the classical gradient-based Levenberg-Marquardt algorithm for a specific catchment, none performs better for the majority of the tested ones. One may also point out a few metaheuristics that do not suit ANN training at all. On the other hand, application of even the simplest ensemble aggregation approach clearly improves the results when the ensemble members are trained by any suitable algorithms.

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

“…Artificial neural networks (ANNs), as the most popular type of data-driven technique, are commonly used in rainfall-runoff modelling (Mondal et al 2012, Valipour et al 2013, Cheng et al 2015, Setiono and Hadiani 2015, Kumar et al 2015, Piotrowski et al 2016, Rezaeianzadeh et al 2016, Shiau and Hsu 2016, Zeroual et al 2016. Due to their flexibility in being able to manage and predict missing data, ANN models are preferred over other conventional methods (Sharma and Tiwari 2009, Siou et al 2012, Zeroual et al 2016.…”