Investigating effect of the preprocessing of the data on the accuracy of the modeling solid waste generation through ANNs

Waste generation in today industries is a serious problem. Waste generation from the production stage to the final disposal is an inevitable issue. Development of the cities and the industrialization causes the everyday increasing in solid waste generation. Therefore, knowing the waste values is an essential tool for solid waste management systems. In this research, artificial neural network is used as a financial tool for modeling solid waste generation in Mashhad. For this purpose, first, some pre-processing on the dependent and independent variables are done and the effect of this procedure on the accuracy of the model is investigated. Research findings clearly indicate that by using some preprocessing on the input data accurate results can be obtained. Three different conditions have been evaluated and the best one is selected which contains logarithm, trend removing and standardizing. The selected network has two hidden layers with five neurons in each one. Network performance parameters are MAPE, MSE and R2 that equals to 0.06, 0.46 and 0.86 respectively.