Prediction of Municipal Solid Waste Generation by Use of Artificial Neural Network: A Case Study of Mashhad

Abstract

Accurate prediction of municipal solid waste’s quality and quantity is crucial for designing and programming municipal solid waste management system. But predicting the amount of generated waste is difficult task because various parameters affect it and its fluctuation is high. In this research with application of feed forward artificial neural network, an appropriate model for predicting the weight of waste generation in Mashhad, was proposed. For this purpose, a time series of Mashhad’s generated waste which have been arranged weekly, from 2004 to 2007, was used. Also, for recognizing the effect of each input data on the waste generation sensitive analysis was performed. Finally, different structures of artificial network were investigated and then the best model for predicting Mashhad’s waste generation was chosen based on mean absolute error (MAE), mean absolute relative error (MARE), root mean square error (RMSE), correlation coefficient (R2) and threshold statistics (TS) indexes. After performing of the mentioned model, correlation coefficient (R2) and mean absolute relative error (MARE) in neural network for test have been achieved equal to 0.746 and 3.18% respectively. Results point that artificial neural network model has more advantages in comparison with traditional methods in predicting the municipal solid waste generation.