skip to main content
Idioma:
Primo Advanced Search
Primo Advanced Search Query Term
Primo Advanced Search prefilters
Busca Simples

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

Abushammala, Mohammed F.M. ; Basri, Noor Ezlin Ahmad ; Elfithri, Rahmah ; Younes, Mohammad K. ; Irwan, Dani

Journal of the Air & Waste Management Association (1995), 2014-02, Vol.64 (2), p.150-159 [Periódico revisado por pares]

United States: Taylor & Francis

Texto completo disponível

Citações Citado por
  • Título:
    Modeling of methane oxidation in landfill cover soil using an artificial neural network
  • Autor: Abushammala, Mohammed F.M. ; Basri, Noor Ezlin Ahmad ; Elfithri, Rahmah ; Younes, Mohammad K. ; Irwan, Dani
  • Assuntos: Algorithms ; Back propagation ; Landfill ; Landfills ; Learning theory ; Mathematical models ; Methane ; Methane - chemistry ; Models, Chemical ; Moisture content ; Neural networks ; Neural Networks (Computer) ; Oxidation ; Oxidation-Reduction ; Soil (material) ; Soil - chemistry ; Soils ; Texture
  • É parte de: Journal of the Air & Waste Management Association (1995), 2014-02, Vol.64 (2), p.150-159
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: 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.
  • Editor: United States: Taylor & Francis
  • Idioma: Inglês
Links
Voltar para lista de resultados
Ir para página anteriorAnterior Resultado  9 AvançarIr para próxima página

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.