A divide-and-conquer method based ensemble regression model for water quality prediction

Abstract

This paper proposes a novel ensemble regression model to predict time series data of water quality. The proposed model consists of multiple regressors and a classifier. The model transforms the original time series data into subsequences by sliding window and divides it into several parts according to the fitness of regressor so that each regressor has advantages in a specific part. The classifier decides which part the new data should belong to so that the model could divide the whole prediction problem into small parts and conquer it after computing on only one part. The ensemble regression model, with a combination of Support Vector Machine, RBF Neural Network and Grey Model, is tested using 450-week observations of CODMn data provided by Ministry of Environmental Protection of the People's Republic of China during 2004 and 2012. The results show that the model could approximately convert the problem of prediction into a problem of classification and provide better accuracy over each single model it has combined. © 2013 Springer-Verlag.