Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam

Nguyen, Xuan Cuong; Nguyen, Thanh-Danh; La, D. Duong; Kumar, Gopalakrishnan; Rene, Eldon R.; Nguyen, Dinh Duc; Chang, Soon Woong; Chung, Woojin; Nguyễn, Xuân Hoàn; Nguyễn, Văn Khánh

doi:10.1016/j.resconrec.2020.105381

Cited by 100 publications

(88 citation statements)

References 39 publications

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“…On the other hand, the ceramic and glass predictive model with the lowest prediction performance requires additional input variables (such as the external window area ratio), apart from the six variables used in this study. Similar findings were also reported for MSW by Johnson et al [34], who applied the GBM algorithm to apply feature compositions differently and developed predictive models for refuse, paper, and MGP (metal, glass, and plastic). Each predictive model developed in that study showed different R 2 and RMSE results depending on the external or internal characteristics of the applied features.…”

Section: Model Performancesupporting

confidence: 84%

“…Thus, bagging can improve the stability and accuracy of machine learning algorithms (MLA) [33]. Recently, RF has been widely utilized as a bagging method for MLA; Cha et al and Nguyen et al [23,34] demonstrated high predictive performance in applying RF to predict waste generation. Boosting, on the other hand, is a technique in which numerous classifiers are generated from early samples, and weak classifiers are collected to generate strong classifiers.…”

Section: Ensemble Modelmentioning

confidence: 99%

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Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables

Cha

Moon

Kim

2021

IJERPH

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Construction and demolition waste (DW) generation information has been recognized as a tool for providing useful information for waste management. Recently, numerous researchers have actively utilized artificial intelligence technology to establish accurate waste generation information. This study investigated the development of machine learning predictive models that can achieve predictive performance on small datasets composed of categorical variables. To this end, the random forest (RF) and gradient boosting machine (GBM) algorithms were adopted. To develop the models, 690 building datasets were established using data preprocessing and standardization. Hyperparameter tuning was performed to develop the RF and GBM models. The model performances were evaluated using the leave-one-out cross-validation technique. The study demonstrated that, for small datasets comprising mainly categorical variables, the bagging technique (RF) predictions were more stable and accurate than those of the boosting technique (GBM). However, GBM models demonstrated excellent predictive performance in some DW predictive models. Furthermore, the RF and GBM predictive models demonstrated significantly differing performance across different types of DW. Certain RF and GBM models demonstrated relatively low predictive performance. However, the remaining predictive models all demonstrated excellent predictive performance at R2 values > 0.6, and R values > 0.8. Such differences are mainly because of the characteristics of features applied to model development; we expect the application of additional features to improve the performance of the predictive models. The 11 DW predictive models developed in this study will be useful for establishing detailed DW management strategies.

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Section: Model Performancesupporting

confidence: 84%

Section: Ensemble Modelmentioning

confidence: 99%

Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables

Cha

Moon

Kim

2021

IJERPH

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“…Among municipal waste management studies, it can be mentioned about some research. For example, Nguyen et al, (2021) compared six data-driven machine-learning methods to predict municipal solid waste generation and random forest and k-nearest neighbor were the most effective algorithms. For readers, Abdallah et al, (2020) published a review of artificial intelligence methods for solid waste management.…”

Section: Problem Definitionmentioning

confidence: 99%

Minimizing Solid Waste Collection Routes Using Ant Colony Algorithm: A Case Study in Gaziantep District

Seçkiner¹,

Shumye²,

Geçer³

2021

jtl

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This paper proposes an ant colony optimization algorithm for a capacitated vehicle routing problem to determine the shortest waste collection and transportation route covered by a waste collection truck in the Şahinbey municipality of Gaziantep/Turkey. The real-case problem concerns a capacity-restricted garbage compactor truck that collects and transports waste from 349 residential waste containers located in 148 collection points. Possible solutions obtained from the ant colony algorithm were compared with mixed-integer programming model solutions. The results of the proposed ant colony optimization algorithm showed that our model yields a 28% reduction in the total daily traveled distances and energy savings against existing consumption. The new solution also cuts the current annual waste collection and transportation expenditure per vehicle by 30%. It is shown that a considerably shorter route distance obtained in the algorithmic solution helps to reduce air pollution from the infamously inefficient garbage collection trucks. Eventually, the new route will considerably reduce labor costs, the high price of fuel, machinery and equipment maintenance as well as environmental pollution, because garbage compactor trucks are one of the least efficient vehicles on the road.

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“…The challenge in data collection is a common issue in both developing and developed countries although data collection systems have progressed for the latter. It is reflected in studies focusing on developing models for waste prediction or forecasting, including China (Duan et al 2020), Vietnam (Nguyen et al 2021), Thailand (Sun and Chungpaibulpatana 2017), India (Kumar and Samadder 2017), South Africa (Ayeleru et al 2021), Canada (Kannangara et al 2018), the Czech Republic (Pavlas et al 2020), Brazil (Teixeira et al 2020), the Russian Federation (Gil'mundinov, Tagaeva, and Boksler 2020), OECD, and EU-28 countries . Waste forecasting is helpful for planning and budget allocation ahead of waste management initiatives.…”

Section: Introductionmentioning

confidence: 99%

“…Kannangara et al (2018) considered eight socio-economic parameters with a correlation ranging from −0.56 to 0.37 for municipal solid waste generation prediction using decision trees and ANN. Nguyen et al (2021) applied random forest and kNN algorithms to the forecasting model and highlighted urban population as the most critical and definitive variable.…”

Section: Introductionmentioning

confidence: 99%

Demographic and socio-economic factors including sustainability related indexes in waste generation and recovery

Fan

Klemeš

Lee

et al. 2021

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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There has been plenty of research on the influence of various socio-economic and demographic data on waste generation to develop effective and targeted waste reduction measures, including energy recovery. This study evaluates the relationship between the waste generation and Circular Material Use rate, Environmental Tax Revenue, and Global Innovation Index beyond the typical socio-economic factors (e.g., gross domestic product or population). Correlation analysis is conducted on the EU-27 datasets before the development of the predictive model. The correlation strength between the factors is discussed to identify the potential rebound effect from the central driver of economic growth and development. A positive correlation and partial rebound effect are identified in the data. The waste amount ending in disposal and energy recovery treatment increases with the Circular Material Use rate, suggesting that the expected gains from Circular Material Use rate are offset by other socio-economic factors such as increasing population or gross domestic product. However, a diminishing trend is observed in the rebound effect over the years. Multiple linear regression with validation is applied to identify the best fit model for predicting waste generation. Using population, gross domestic product, Circular Material Use rate, and Environmental Tax Revenues as independent variables, a model is generated with a mean absolute percentage error of 18.65% (7% lower than the benchmark) and R 2 (coefficient of determination) of 0.995.

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Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam

Cited by 100 publications

References 39 publications

Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables

Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables

Minimizing Solid Waste Collection Routes Using Ant Colony Algorithm: A Case Study in Gaziantep District

Demographic and socio-economic factors including sustainability related indexes in waste generation and recovery

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