Predicting the Quantity of Municipal Solid Waste using XGBoost Model

Jayaraman, Vaishnavi; Parthasarathy, Saravanan; Raj, L. Arun; Singh, Hiran Kumar

doi:10.1109/icirca51532.2021.9544094

Cited by 9 publications

(3 citation statements)

References 10 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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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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“…Since its introduction, XGBoost has won various data competitions and has served as the driving force behind several cutting-edge industrial applications, such as real-time traffic accident detection, PM2.5 concentration prediction, and COVID-19 prediction . Additionally, in waste-related fields, researchers have used the XGBoost algorithm to solve several tasks, including predicting the quantity of municipal solid waste . However, to the best of our knowledge, no study has combined XGBoost and HSI to identify solid waste.…”

Section: Introductionmentioning

confidence: 99%

Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Lan

et al. 2023

ACS EST Eng.

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Waste material identification is an essential part of waste recycling and treatment. Hyperspectral imaging (HSI) enables fast, accurate, nondestructive, and non-invasive identification of waste materials. In this study, HSI-based classification of typical industrial organic waste that cannot be sorted via traditional methods has been explored, namely, leather, paper, plastic, rubber, textile, and wood. The extreme gradient boosting (XGBoost) algorithm, a supervised machine learning algorithm that has never been investigated for waste identification-related fields, was adopted. The results show that XGBoost obtained a higher pixelwise weighted average F1-score of 82.72% and a faster prediction time of 270 ms for the tested images compared with the commonly used partial least squares-discriminant analysis (77.83% and 444 ms). XGBoost was more effective and efficient in aiding HSI identification and classification of industrial organic waste. The technique can be a significant advancement in the development of an online sorting or identification platform, affording significant labor cost reduction, time savings, and the provision of a stable, accurate, and rapid method for waste intelligent identification.

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“…The K-nearest neighbor (KNN), RF, and DNN algorithms were applied through hyper-parameters adjustment, and the resulting R 2 were 0.96, 0.97, and 0.91, respectively. Jayaraman et al [26] developed an MSW predictive model using SARIMA (season ARIMA) and XGboost (extreme gradient boosting) algorithms, in conjunction with a timeseries dataset of MSW (1129 rows and 40 columns) from 2006 to 2018, revealing that XGboost (R 2 = 0.4145) was superior to SARIMA (R 2 = −0.8885). Moreover, the prediction performance of XGboost was improved by adjusting the tree number and max-depth.…”

Section: Introductionmentioning

confidence: 99%

Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate

et al. 2023

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Smart management of construction and demolition (C&D) waste is imperative, and researchers have implemented machine learning for estimating waste generation. In Korea, the management of demolition waste (DW) is important due to old buildings, and it is necessary to predict the amount of DW to manage it. Thus, this study employed decision tree (DT)-based ensemble models (i.e., random forest—RF, extremely randomized trees—ET, gradient boosting machine—GBM), and extreme gradient boost—XGboost) based on data characteristics (i.e., small datasets with categorical inputs) to predict the demolition waste generation rate (DWGR) of buildings in urban redevelopment areas. As a result of the study, the RF and GBM algorithms showed better prediction performance than the ET and XGboost algorithms. Especially, RF (6 features, 450 estimators; mean, 1169.94 kg·m−2) and GBM (4 features, 300 estimators; mean, 1166.25 kg·m−2) yielded the top predictive performances. In addition, feature importance affecting DWGR was found to have a significant impact on the order of gross floor area (GFA) > location > roof material > wall material. The straightforward collection of features used here can facilitate benchmarking as a decision-making tool in demolition waste management plans for industry stakeholders and policy makers. Therefore, in the future, it is required to improve the predictive performance of the model by updating additional data and building a reliable dataset.

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Predicting the Quantity of Municipal Solid Waste using XGBoost Model

Cited by 9 publications

References 10 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

Application of XGBoost for Fast Identification of Typical Industrial Organic Waste Samples with Near-Infrared Hyperspectral Imaging

Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate

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