Municipal Solid Waste Forecasting in China Based on Machine Learning Models

Yang, Liping; Zhao, Yaqi; Niu, Xiaxia; Song, Zisheng; Gao, Qingzhu; Wu, Jun

doi:10.3389/fenrg.2021.763977

Cited by 13 publications

(5 citation statements)

References 36 publications

(65 reference statements)

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“…Our literature review highlights a variety of methods and variables employed in forecasting MSW generation, as showcased in Table 1 [5,9,[14][15][16][17][18][19][20]. Notably, Wu et al (2020) demonstrated the significance of geographic differentiation in achieving accurate predictions through their regional approach in China [14].…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

confidence: 99%

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

Chen,

Chang

2024

Sustainability

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“…Yang et al (2021) conducted a comparison of six machine learning models for municipal solid waste prediction (MSW) in China [11]. The models included Multiple Linear Regression (MLR), Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boosting (XGBOOST), K-Nearest Neighbor (KNN), and Artificial Neural Network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The data include Total Regional Gross Domestic Product (GDP), Value Added by Transportation, Warehouse, and Postal Services, Wholesale and Retail Value Added, Value Added by the Accommodation and Catering Industry, City Area, Urban Population Density, the Number of Urban Populations, Urban Per Capita Disposable Income, and Total Retail Sales of Consumer Goods. The regional GDP indicator is widely acknowledged as the most significant variable in predicting waste production [11]. In the authors' previous study, a Neural Network was employed for predicting the volume of municipal solid waste in Poland.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Kulisz,

Kujawska,

Cioch

et al. 2024

Applied Sciences

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In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges.

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“…The author also reported the weather variables and building type and density as the most influential features for weekly waste generation predictions. Similarly, Yang et al [29] proposed a novel predictive approach to unravel the effect of socioeconomic characteristics on solid waste generation. The study collected annual statistical waste generation data and nine socioeconomic factors of all provinces in mainland China between 2008 to 2019.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation

Namoun

Tufail

Alrehaili

et al. 2022

Sensors

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With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed.

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Municipal Solid Waste Forecasting in China Based on Machine Learning Models

Cited by 13 publications

References 36 publications

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

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

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation

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