Comparative Analysis of the Implementation of Support Vector Machines and Long Short-Term Memory Artificial Neural Networks in Municipal Solid Waste Management Models in Megacities.

The development of methodologies to support decision-making in municipal solid waste (MSW) management processes is of great interest for municipal administrations. Artificial intelligence (AI) techniques provide multiple tools for designing algorithms to objectively analyze data while creating highly precise models. Support vector machines and neuronal networks are formed by AI applications offering optimization solutions at different managing stages. In this paper, an implementation and comparison of the results obtained by two AI methods on a solid waste management problem is shown. Support vector machine (SVM) and long short-term memory (LSTM) network techniques have been used. The implementation of LSTM took into account different configurations, temporal filtering and annual calculations of solid waste collection periods. Results show that the SVM method properly fits selected data and yields consistent regression curves, even with very limited training data, leading to more accurate results than those obtained by the LSTM method.

Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks.

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.

Estudio comparativo de las normas relevantes a nivel internacional para la definición, clasificación, exclusión, desclasificación e identificación de residuos peligrosos / Comparative study of the international significant standards for the definition, exclusion, declassification and identification of hazardous wastes

El presente artículo realiza una comparación de las normas relevantes a nivel internacional para la definición, clasificación, exclusión, desclasificación e identificación de residuos peligrosos. Dentro de los principales sistemas de clasificación de residuos peligrosos se encuentran: La Convención de Basilea sobre el control de los movimientos transfronterizos de los desechos peligrosos y su eliminación, El Listado Europeo de Residuos (LER) y el Código de Regulación Federal de los Estados Unidos 40 CFR 261. Las tres normas, presentan grandes diferencias en relación a los criterios de clasificación e identificación de un residuo peligroso y, por tanto, frente a sus propios listados de clasificación. Por esta razón, el presente trabajo pretende realizar un análisis crítico comparativo entre las tres regulaciones con el objeto de analizar las ventajas e inconvenientes en relación a la definición, identificación, clasificación, exclusión y desclasificación de residuos peligrosos.

In this article the international important standards of hazardous wastes are compared including definition, exclusion, declassification and identification. Some of the main hazardous wastes classification systems are: Basel convention about the control of cross-border movement of hazardous waste and its elimination, the European list of wastes (LER) and the U.S code of federal regulations 40 CFR 261. The three standards have big differences regarding the classification criteria and identification of hazardous waste therefore about their own classification lists. For this reason, this article aims to make a critical and comparative analysis between the three regulations to analyze the advantages and disadvantages in regard to different aspects related to this problematic (definition, exclusion, declassification and identification of hazardous wastes).