Compression ratio of municipal solid waste simulation using artificial neural network and adaptive neurofuzzy system

The compression ratio of Municipal Solid Waste (MSW) is an essential parameter for evaluation of waste settlement. Since it is relatively time-consuming to determine compression ratio from oedometer tests and there exist difficulties associated with working on waste materials, it will be useful to develop models based on waste physical properties. Therefore, present research attempts to develop proper prediction models using ANFIS and ANN models. The compression ratio was modeled as a function of the physical properties of waste including dry unit weight, water content, and biodegradable organic content. A reliable experimental database of oedometer tests, taken from the literature, was employed to train and test the ANN and ANFIS models. The performance of the developed models was investigated according to different statistical criteria (i.e. correlation coefficient, root mean squared error, and mean absolute error) recommended by researchers. The final models have demonstrated the correlation coefficients higher than 90% and low error values; so, they have capability for acceptable prediction of municipal solid waste compression ratio. Furthermore, the values of performance measures obtained for ANN and ANFIS models indicate that the ANFIS model performs better than ANN model.