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首页> 外文期刊>Waste Management >Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation
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Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation

机译：多级神经网络方法在缺少数据归档时序列垃圾填埋气模拟的应用

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摘要

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 (CH_4) data for applying in time series artificial neural network (ANN) model in an attempt to predict daily CH_4 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 CH_4 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 CH_4 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 CH_4 generation prediction.
机译：为了减轻温室气体效果，需要精确且精确的垃圾填埋气预测模型，以便更精确地预测填埋场甲烷气体的量和恢复时间。当研究与温室气体排放问题相关时，时间序列预测模型具有相当大的兴趣，其中需要大量的气体数据记录。本研究首先是专门赋予缺失的甲烷（CH_4）数据，以便在时间序列人工神经网络（ANN）模型中，试图预测来自加拿大Regina，SK的垃圾填埋场的日常CH_4代速率。在数据上进行预处理，以评估独立和显着的气象输入变量，并为开发CH_4代模型提供合适的数据集。通过在第一阶段缺少数据载荷来执行本研究中提出的两阶段时间序列模型，然后在第二阶段进行神经网络自动回归模型，其具有外源输入（NARX）。隐藏层中有3层，5种气候变量和9个神经元的模型是最佳结构。该模型显示了CH_4预测中的高性能，平均达到0.92的平均指数，平均平均值在测试阶段期间的3.03％的平均值百分比误差（MAPE）。缺少数据载荷与NARX方法耦合的模型的平均平方误差（MSE）减少84％（与Multidayer Perceptrons神经网络模型相比）在测试时段中表示缺失数据估计与日常CH_4中的时间序列ANN模型的有效性生成预测。

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