| Popis: |
The study compared the effectiveness of the rough set theory and artificial neural networks with respect to predicting the rate of waste mass accumulation for recipients in the areas of rural municipalities. Simulations were performed for two variants of input variables. The first of them used all economic, infrastructure and economic indicators as independent variables. The second case was limited only to those whose correlation with the class label attribute exceeded 0,2 and they included: population density, percentage of buildings in the municipality covered by the collection system, the rate of income, and agricultural area. The analysis showed that rough sets’ models generate comparable-quality forecasts of mass waste accumulation rate for rural municipalities, such as artificial neural networks. The developed models are characterized by a high forecast error of about 20%–27%. Further research is needed towards finding effective methods or other conditional attributes that describe the rate of mass accumulation of waste in the areas of rural municipalities. |
