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

Abstract

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.Implications: To date, no attempts have been made to predict the percent of CH 4 oxidation within landfill cover soils using an ANN. This paper presents modeling of CH 4 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 CH 4 oxidation from other landfills with similar climate conditions, cover soil texture, and other properties. The predicted value of CH 4 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 CH 4 emission and how much it contributes to global warming.

From California dreaming to California data: Challenging historic models for landfill CH4 emissions

“…Finally, some recent studies have also proposed the use of artifi cial neural networks (ANN) to account for overall soil complexity in the absence of robust mechanistic models addressing interrelated factors ( Young et al, 2001 ). As an example, Abushammala et al(2013a) utilized an ANN to predict the percentage of oxidation for a particular landfi ll, which they assumed could account for a variety of climatic and soil properties at a particular site, then proposed inserting this improved percentage in the IPCC guidelines in place of the current 10% default value ( Abushammala et al, 2013b ). However, ANN models would require separate training (calibration) for diff erent soil textures, climates, and cover geometries, greatly complicating their application.…”

Solid waste forecasting using modified ANFIS modeling

“…RMSE (Lin et al, 2013) and the coefficient of determination (R 2 ) were used to evaluate the appropriate input selection and model performance and ability to produce precise forecast (El-Shafie et al, 2011, Abushammala et al, 2014.…”

Application of machine learning algorithms in municipal solid waste management: A mini review

“…Although these studies claimed to have good results, the lack of comparisons with similar methods makes it difficult to be convincing. Only a few studies used the results in other papers as a comparison baseline directly, but due to the different datasets used, such a comparison is not convincing (Abushammala et al, 2014; Bagheri et al, 2019; Kannangara et al, 2018; Togacar et al, 2020). Recently, Mao et al (2020) compared the performance of different CNN models proposed by several studies based on the same dataset of TrashNet, proving that their model can achieve better accuracy.…”