Solid Waste Management in Peru’s Cities: A Clustering Approach for an Andean District

Abstract

There is a great deficiency in the collection and disposal of solid waste, with a considerable amount disposed of in dumps instead of in landfills. In this sense, the objective of this research is to propose a solid waste mitigation plan through recovery in the District of Santa Rosa, Ayacucho. For this, a solid waste characterization plan was executed in eight days, and through ANOVA it was shown that there is a significant difference in means between business pairs except between a bakery and a hotel. Through clustering, zones A and B are highly correlated, reflecting that the amount of organic waste was greater than inorganic waste. In the organic waste valorization plan, the results through ANOVA indicate a significant difference for monthly and daily averages, and the clustering shows the different behavior of each month, drawing attention to August, concluding that the valorization pilot plan is viable due to the contribution of a large amount of organic solid waste to the valorization plant.

Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models

“…Furthermore, the findings of this study indicate that the proposed combination model outperformed other methods in capturing the complex and nonlinear behavior of crude oil prices. This suggests that the hybrid forecasting approach holds promise for applications beyond crude oil prices (for example, energy [47,48], air pollution [49][50][51], solid waste [52], academic performance [53] and digital marketing [54]). Therefore, it is recommended to employ this methodology for forecasting other complex financial time series data, such as inflation, unemployment, and cryptocurrencies.…”

Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method

“…Furthermore, since only linear and non-linear univariate and multivariate time series models were used in this work, machine learning models such as deep learning and artificial neural networks can also be considered within the current decomposition combination forecasting framework. It can also be extended and applied to other approaches and datasets (for example, energy [53][54][55], air pollution [56][57][58][59], solid waste [60] and academic performance [61]).…”

Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values

“…First, the technique is developed to analyze environmental pollution data based on univariate time series from various sources. Further studies are required for its validation in other contexts (for example, in data related to air quality [59][60][61], solid waste [62], also in academic performance data [63], data related to digital marketing [64] or those based on energy efficiency [65,66]). One of the methodology's significant limitations is that it does not preserve the time series structure since it assumes an auto-regressive model with a predefined lag size.…”