Modeling of methane oxidation in landfill cover soil using an artificial neural network

摘要

Knowing the fraction of methane (CH_4) oxidized in landfill cover soils is an important step in estimating the total CH_4 emissions from any landfill. Predicting CH_4 oxidation in landfill cover soils is a difficult task because it is controlled by a number of biological and environmental factors. This study proposes an artificial neural network (ANN) approach using feedforward backpropagation to predict CH_4 oxidation in landfill cover soil in relation to air temperature, soil moisture content, oxygen (O_2) concentration at a depth of 10 cm in cover soil, and CH_4 concentration at the bottom of cover soil. The optimum ANN model giving the lowest mean square error (MSE) was configured from three layers, with 12 and 9 neurons at the first and the second hidden layers, respectively, log-sigmoid (logsig) transfer function at the hidden and output layers, and the Levenberg-Marquardt training algorithm. This study revealed that the ANN oxidation model can predict CH_4 oxidation with a MSE of 0. 0082, a coefficient of determination (R~2) between the measured and predicted outputs of up to 0.937, and a model efficiency (E) of 0.8978. To conclude, further developments of the proposed ANN model are required to generalize and apply the model to other landfills with different cover soil properties.