Forecasting Municipal Solid waste Generation by Hybrid Support Vector Machine and Partial Least Square Model

Forecasting of municipal waste generation is a critical challenge for decision making and planning, because proper planning and operation of a solid waste management system is intensively affected by municipal solid waste (MSW) streams analysis and accurate predictions of solid waste quantities generated. Due to dynamic and complexity of solid waste management system, models by artificial intelligence can be a useful solution of this problem. In this paper, a novel method of Forecasting MSW generation has been proposed. Here, support vector machine (SVM) as an intelligence tool combined with partial least square (PLS) as a feature selection tool was used to weekly prediction of MSW generated in Tehran, Iran. Weekly MSW generated in the period of 2008 to 2011 was used as input data for model learning. Moreover, Monte Carlo method was used to analyze uncertainty of the model results. Model performance evaluated and compared by statistical indices of Relative Mean Errors, Root Mean Squared Errors, Mean Absolute Relative Error and coefficient of determination. Comparison of SVM and PLS-SVM model showed PLS-SVM is superior to SVM model in predictive ability and calculation time saving. Also, results demonstrate which PLS could successfully identify the complex nonlinearity and correlations among input variables and minimize them. The uncertainty analysis also verified that the PLS-SVM model had more robustness than SVM and had a lower sensitivity to change of input variables.