Prediction for the Solid Waste Composition by use of Different Curve Fitting Models: A Case Study

Abstract

This study presents a methodology for the prediction of solid waste composition in the urban area based on a set of limited samples. The methodology was applied by a case study for Eskisehir city in Turkey. For this purpose, Municipal Solid Waste (MSW) samples were collected for one year according to socioeconomic structure of districts. MSW samples were separated mainly into five groups of: paper-cardboard, metals, glass, plastics and food wastes as manually. The 75% of the values for each group were used as train data sets and the remains were used as test sets considering to income levels and population. It was used different curve fitting models for training of data and obtained different equations (power, exponential and polynomial) from the models. These equations were used for prediction of test sets and real values and test results were compared. Prediction accuracies were determined and interpreted according to different goodness of measurement values. It was seen that the effect of income level and population on waste composition from the degree of accuracy of this model is very important.