Publications

Environmental, Social, and Economic Implications of Global Reuse and Recycling of Personal Computers

Author(s): Ramzy Kahhat Abedrabbo y otros

Market dynamics and environmental impacts of e-commerce: A case study on book retailing

Author(s): Ramzy Kahhat Abedrabbo y otros

Exploring e-waste management systems in the United States

Author(s): Ramzy Kahhat Abedrabbo y otros

Development of a Framework for Quantifying the Environmental Impacts of Urban Development and Construction Practices

Author(s): Ramzy Kahhat Abedrabbo y otros

ICV de la minería artesanal del oro

Author(s): Red Peruana Ciclo de Vida

Análisis de ciclo de vida de los biocombustibles en el Perú

Author(s): Red Peruana Ciclo de Vida

(Español) Applying random forest to forecast municipal solid waste generation from household fuel consumption

Author(s): Luis Izquierdo Horna, Ramzy Kahhat Abedrabbo, Ian Vázquez Rowe

(Español) Accurately forecasting municipal solid waste (MSW) generation is essential for designing efficient waste management systems and promoting sustainable urban development. As cities expand and consumption patterns shift, reliable data-driven approaches are increasingly necessary to address the complexities of MSW generation. This study applied the random forest (RF) algorithm, a machine learning technique, to predict MSW generation at the household level. RF was selected for its capacity to handle non-linear relationships, imbalanced datasets, and outliers. The analysis focused on data from 2019, avoiding distortions associated with the COVID-19 pandemic. The model integrated per capita MSW data with household fuel consumption indicators (i.e., natural gas, electricity, and liquefied petroleum gas) and demographic variables such as age, education level, and monthly expenditure. The case study focused on the city of Lima, Peru, using 80 % of the data for training and 20 % for testing, with hyperparameters optimized via 5-fold cross-validation. The final model explained 55 % of the variance in MSW generation (R² = 0.55). This result reflects the model’s ability to capture significant drivers of variability, although it leaves room for refinement due to factors not included in the analysis, such as cultural practices or seasonality. Among the predictors, household monthly expenditure on cooking fuels emerged as the most influential variable, reinforcing the connection between resource consumption and waste generation. These findings highlight the potential of integrating socioeconomic indicators into predictive models to enhance their reliability. By improving forecasting capabilities, this study supports targeted policies for urban waste management and sustainable resource use.