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- W2211547189 abstract "The main obstacle in managing the Urban Solid Waste (USW) is the poor level of knowledge of basic data Available data on level and quality of USW production are often insufficient and, sometimes, whole data are missing. Moreover, the knowledge of historical data does not solve all our problems. In fact simple statistical methods for a direct forecast or an extrapolation from the available data give rise to scarcely reliable outcomes, because they take no notice of the real production factors. In brief, the substantial ignorance of USW production characteristics is mainly due to the difficulties in making reliable USW generation models. The most reliable classical models are very complex and involve a wide and heterogeneous amount of data. Moreover, the USW generation mechanisms are not in agreement with definite rules. In this work a fuzzy-genetic model for a reliable estimate of qualitative and quantitative USW production data is proposed, in order to overcome the above difficulties. More precisely, the outcome model, realized by fuzzy set theory and genetic algorithms, appear effective for both estimating data on several geographical scales and forecasting future data; it remarkably reduces computational complexity and the amount of required input data, compared with Other As it is well known, by fuzzy set theory it is possible to deal with problems affected by uncertainty and to obtain reliable models for non linear phenomena whose characterization is based on rough and poor data. The determination of a fuzzy model can be obtained by the contribution of an expert of the field able to give knowledge base. The model can be as better as the expert will consider all necessary parameters of the process. Such a limitation can be overcome by genetic algorithms which allow to individualize an optimal fuzzy, model." @default.
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- W2211547189 date "1997-04-02" @default.
- W2211547189 modified "2023-09-27" @default.
- W2211547189 title "Urban Solid Waste Generation Model Via Fuzzy-Genetic Algorithm" @default.
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- W2211547189 doi "https://doi.org/10.2495/mmep970511" @default.
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