Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.
Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate.
Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, Fars 71348-51156, Iran.
Sama
S
Azadi
Department of Civil and Environmental Engineering, School of Engineering, Shiraz University, Shiraz, Fars 71348-51156, Iran. Electronic address: akarimi@shirazu.ac.ir.
Ayoub
A
Karimi-Jashni
D007492:Iran; D008962:Models, Theoretical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D012044:Regression Analysis; D012621:Seasons; D062611:Solid Waste / Q000032:analysis*; D018505:Waste Management / Q000379:methods*
D062611:Solid Waste
Artificial neural network; Fars Province; Multiple linear regression; Seasonal municipal solid waste generation