Application of periodic parameters and their effects on the ANN landfill gas modeling.

Abstract

To reach a practical landfill gas management system and to diminish the negative environmental impacts from landfills, accurate methane (CH4) prediction is essential. In this study, the preprocessing steps including minimizing multicollinearity, removal of outliers, and errors with missing data imputation are applied to enhance the data quality. This study is the first at employing periodic parameters in the two-stage non-linear auto-regressive model with exogenous inputs (NARX) with the aim of providing a convenient and precise approach to predict the daily CH4 collection rate from a municipal landfill in Regina, SK, Canada. Using a stepwise procedure, various volumes of training data were assessed, and concluded that employing the 3-year training data reduced the mean absolute percentage error (MAPE) of the CH4 prediction model by 26.97% at the testing stage. The favorable artificial neural network model performance was obtained using the day of the year (DOY) as a sole input of the time series model with MAPE of 2.12% showing its acceptable ability in CH4 prediction. Using an only DOY-based model is especially remarkable because of its simplicity and high accuracy showing a convenient and effective approach in time landfill gas modeling, particularly for the landfills with no reliable climatic data.