An ARIMA and XGBoost Model Utilized for Forecasting Municipal Solid Waste Generation

Javid, Irfan; Ghazali, Rozaida; Batool, Tuba; Jafri, Syed Irteza Hussain; Altaf, Abdullah

doi:10.1007/978-3-031-43838-7_2

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…In our study, we apply the XGBoost model, drawing from its demonstrated success in environmental forecasting as seen in referenced studies [24][25][26]. XGBoost's effectiveness in predicting municipal solid waste (MSW) generation was highlighted in Multan, Pakistan [24], and Northern Ireland [25], where it outperformed other models with higher R 2 values and lower RMSEs. Additionally, its application in air quality assessment, specifically in predicting PM2.5 concentrations in Tehran [26], showcased its precision in environmental health scenarios.…”

Section: Model Development By Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Leveraging Circular Economy Metrics for Data-Driven Forecasting of Solid Waste Production in Europe

Chen,

Chang

2024

Sustainability

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This study integrates circular economy (CE) metrics with machine learning techniques, specifically XGBoost and Shapley additive explanations (SHAP), to forecast municipal solid waste (MSW) in the EU, analyzing data from 2010 to 2020. It examines key economic and consumption indicators, including GDP per capita and energy consumption, along with CE metrics such as resource productivity, the municipal waste recycling rate, and the circular material use rate. The model demonstrates high predictive accuracy, with an R2 of 99% for in-sample data and 75% for out-of-sample data. The results indicate a significant correlation between a higher GDP per capita and an increased gross municipal waste per capita (GMWp). Conversely, lower energy consumption is associated with reduced GMWp. Notably, the circular material use rate emerges as a crucial factor for sustainability, with increased use significantly decreasing the GMWp. In contrast, a higher resource productivity correlates with an increased GMWp, suggesting complex implications for waste generation. The recycling rate, while impactful, shows a more modest effect compared to the other factors. The culminating insights from this study emphasize the need for sustainable, integrated waste management and support the adoption of circular economy-aligned policies. They underscore the efficacy of merging CE metrics with advanced predictive models to bolster regional sustainability efforts.

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