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.
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Data availability
The data that support the findings of this study are available from City of Regina Solid Waste Department but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of City of Regina Solid Waste Department.
Abbreviations
- ANN:
-
Artificial neural network
- CH4:
-
Methane
- CO2 :
-
Carbon dioxide
- DOY:
-
Day of the year
- DPMax:
-
Maximum dew point
- DPMean:
-
Mean dew point
- DPMin:
-
Minimum dew point
- GHG:
-
Greenhouse gas
- H Max :
-
Maximum relative humidity
- H Min :
-
Minimum relative humidity
- IA:
-
Index of agreement
- IQR:
-
Inter-quartile range
- LFG:
-
Landfill gas
- LM:
-
Levenberg-Marquardt
- MAPE:
-
Mean absolute percentage error
- MLP:
-
Multilayer perceptrons
- MOY:
-
Month of the year
- MSE:
-
Mean square error
- NARX:
-
Non-linear auto-regressive model with exogenous inputs
- P Max :
-
Maximum air pressure
- P Min :
-
Minimum air pressure
- Q1:
-
First quartiles
- Q3:
-
Third quartiles
- R :
-
Correlation coefficient
- R 2 :
-
Coefficient of determination
- RMSE:
-
Root mean square error
- SCADA:
-
Supervisory Control and Data Acquisition
- T Max :
-
Maximum temperature
- T Mean :
-
Mean temperature
- T Min :
-
Minimum temperature
- W Max :
-
Maximum wind speed
- W Min :
-
Minimum wind speed
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Acknowledgments
Acknowledgment goes to the team at the City of Regina Solid Waste Department, who supported the data collection and Dr. Kelvin Ng’s research team for data collection. The views expressed herein are those of the writers and not necessarily those of our research and funding partners. The financial support to the first author of this manuscript in the form of graduate research scholarship and PhD award is greatly acknowledged.
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The financial support to the first author of this manuscript in the form of graduate research scholarship and PhD award is greatly acknowledged.
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Bahareh Fallah: Conceptualization, Formal analysis, Writing and Original draft preparation. Farshid Torabi: Supervision, Review & Editing.
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Fallah, B., Torabi, F. Application of periodic parameters and their effects on the ANN landfill gas modeling. Environ Sci Pollut Res 28, 28490–28506 (2021). https://doi.org/10.1007/s11356-021-12498-5
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DOI: https://doi.org/10.1007/s11356-021-12498-5