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

Knowing the fraction of methane (CH4) oxidized in landfill cover soils is an important step in estimating the total CH4 emissions from any landfill. Predicting CH4 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 CH4 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 CH4 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 CH4 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. Implications: To date, no attempts have been made to predict the percent of CH4 oxidation within landfill cover soils using an ANN. This paper presents modeling of CH4 oxidation in landfill cover soil using ANN based on field measurements data under tropical climate conditions in Malaysia. The proposed ANN oxidation model can be used to predict the percentage of CH4 oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH4 oxidation can be used in conjunction with the Intergovernmental Panel on Climate Change (IPCC) First Order Decay (FOD) model by landfill operators to accurately estimate total CH4 emission and how much it contributes to global warming.