Ordonez Ponce, E.2010-12-102004https://hdl.handle.net/10182/3004While the Chilean constitution guarantees the right of a clean environment and that environmental acts and policies to manage waste have been passed, waste generation has increased dramatically in the last decade in Chile and programmes to recycle, recover or reuse waste are not being implemented. The extent of Chile's waste management problem is vast. Among the existing problems for implementing waste management programmes in Chile is the lack of information on factors contributing to waste generation and the absence of waste generation forecasts. Recognising these waste generation factors is essential for implementing policies to reduce waste generation and waste generation forecasts are fundamental for planning waste management systems. This research aims to design an analysis tool to assess waste generating factors and forecast waste generation for a significant portion of Chile. Data for many variables indicating socio-demographic, economic, geographic and waste-related conditions were collected based on the existing literature. Using these variables, statistical methods identified Population, Percentage of Urban Population, Years of Education, Number of Libraries and Number of Indigents as the most important factors contributing to waste generation in Chile. A Multi-Layer Perceptron neural network modelled the relationship between these variables and waste generation with great accuracy (R² = .819). The MLP network determined their respective contribution to waste generation and showed that they all contribute positively to waste generation. Using these variables, a Self-Organising Feature Map neural network clustered the 342 communes of Chile into three groups (with 91, 156 and 95 communes) from which representative communes were selected for data collection for forecasting waste generation. The most representative communes were not used due to lack of data. Therefore, secondary representative communes were selected, reducing the level of representativeness of the model from 230 (67.3%) communes to 167 (48.8%). Data was collected from the secondary communes and forecasts for waste generation up to the year 2010 were made. Recurrent networks were the best neural networks for forecasting waste generation using the selected variables for the three groups (R² = 0.75, 0.25 and 0.80, respectively). These results were improved using Multi Layer Perceptrons and recurrent networks with Per Capita Waste Generation as a new input (R² = 0.81, 0.91 and 0.98), showing extremely accurate forecasts for the validation periods. Forecasted rates show that by 2010, representative communes will generate 100, 240 and 2,900 tonnes/month, reaching annual rates of 1%, 0.6% and -3%, respectively. The forecasted results were used to obtain estimates for the represented communes of each group. Total waste generation from the represented communes will peak at 3,800 tonnes/month and 18,500 tonnes/month by 2010 and over 330,000 tonnes/month by 2007. Extrapolating these results shows that Chile will peak at more than 500,000 tonnes/month by 2007, an increase of 7.6% in total waste generation from 2002. Finally, it has been demonstrated that artificial neural networks have the potential to work with waste data to great accuracy despite the problems with the data. The proposed model represents a reliable tool for improving waste management not only for Chile, but also abroad.1-198enwaste generationclusteringartificial neural networksforecastingmulti-layer perceptronself-organising feature mapsrecurrent networksChileThesisQ112860131