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• W3048301386 abstract "To mitigate the greenhouse gas effect, accurate and precise landfill gas prediction models are required for more precise prediction of the amount and recovery time of methane gas from landfills. When the study associates to greenhouse gas emissions problems, time series prediction models are of considerable interests, in which significant past records of gas data are required. This study is the first to specially impute the missing methane (CH4) data for applying in time series artificial neural network (ANN) model in an attempt to predict daily CH4 generation rate from a landfill in Regina, SK, Canada. Pre-processing was conducted on data to evaluate independent and significant meteorological input variables and provide suitable dataset for developing CH4 generation models. A two-stage time series model proposed in this study was performed by missing data imputation at the first stage, followed by a neural network auto-regressive model with exogenous inputs (NARX) at the second stage. The model with 3 layers, 5 climatic variables and 9 neurons in the hidden layer was the optimal structure. This model shows the high performance in CH4 prediction with the average index of agreement of 0.92 and the average mean absolute percentage error (MAPE) of 3.03% during the testing stage. Missing data imputation coupled with NARX method decreased the mean squared error (MSE) of the model by 84% (compared to Multilayer Perceptrons neural network model) in the testing period representing the effectiveness of missing data estimation coupling with time series ANN models in daily CH4 generation prediction." @default.
• W3048301386 created "2020-08-13" @default.
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• W3048301386 date "2020-10-01" @default.
• W3048301386 modified "2023-10-03" @default.
• W3048301386 title "Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation" @default.
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• W3048301386 doi "https://doi.org/10.1016/j.wasman.2020.07.034" @default.
• W3048301386 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32784123" @default.
• W3048301386 hasPublicationYear "2020" @default.
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