The results show a high probability of not meeting the groundwater quality standards when 25 deriving a policy from just a deterministic analysis. To increase the reliability several 26 realizations can be optimized at the same time. By using a mixed-integer stochastic 27 formulation, the desired reliability level of the strategy can be fixed in advance. The approach 28 allows deriving the trade-offs between the reliability of meeting the standard and the net 29 benefits from agricultural production. In a risk-averse decision-making, not only the reliability 30 of meeting the standards counts, but also the probability distribution of the maximum pollutant 31 concentrations.A sensitivity analysis was carried out to assess the influence of the variance of 32 the hydraulic conductivity fields on the strategies.The results have shown that larger the 33 variance, greater the range of maximum nitrate concentrations and the worst-case (or maximum 34 value) that could be reached for the same level of reliability. 35
This study presents an analysis of three models associated with artificial intelligence as tools to forecast the generation of urban solid waste in the city of Bogotá, in order to learn about this type of waste's behavior. The analysis was carried out in such a manner that different efficient alternatives are presented. In this paper, a possible decision-making strategy was explored and implemented to plan and design technologies for the stages of collection, transport and final disposal of waste in cities, while taking into account their particular characteristics. The first model used to analyze data was the decision tree which employed machine learning as a non-parametric algorithm that models data separation limitations based on the learning decision rules on the input characteristics of the model. Support vector machines were the second method implemented as a forecasting model. The primary advantage of support vector machines is their proper adjustment to data despite its variable nature or when faced with problems with a small amount of training data. Lastly, recurrent neural network models to forecast data were implemented, which yielded positive results. Their architectural design is useful in exploring temporal correlations among the same. Distribution by collection zone in the city, socio-economic stratification, population, and quantity of solid waste generated in a determined period of time were factors considered in the analysis of this forecast. The results found that support vector machines are the most appropriate model for this type of analysis.
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