Prediction of Tehran solid waste production by using of neural network and adaptive neuro-fuzzy inference system

Document Type : Research Paper



In this study two intelligent systems, based on adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANNs) of forecasting municipal solid wastes (MSW) generation has been proposed. ANFIS and ANNs as an intelligent tool compared with together was used to monthly prediction of MSW generated in Tehran. Monthly amount of solid wastes (SW), total monthly precipitation, monthly mean temperature, monthly average humidity and the rank of months per year in the period of 2009 -2014 was used as input data for model learning. The best ANN model had a 5-14-1 structure, it consisted of an input layer with five input variables, one hidden layers with 14 neurons and MSW production as output. The best ANFIS model was designed using one ANFIS architecture with the six-time run out which were developed at three stages. Correlation coefficient (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE) for the best ANN model were computed as 0.825, 0.132 and 1.19, respectively. The corresponding R2, RMSE and MAPE values for the best ANFIS topology were 0.963, 0.096 and 1.05 respectively. The results of this study showed that, multi-layer ANFIS model due to employing fuzzy rules, better performance than the ANN model.


Main Subjects

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