Municipal Solid Waste Forecasting in China Based on Machine Learning Models

As the largest producing country of municipal solid waste (MSW) around the world, China is always challenged by a lower utilization rate of MSW due to a lack of a smart MSW forecasting strategy. This paper mainly aims to construct an effective MSW prediction model to handle this problem by using mac...

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Main Authors:	Liping Yang, Yigang Zhao, Xiaxia Niu, Zisheng Song, Qingxian Gao, Jun Wu
Format:	article
Language:	EN
Published:	Frontiers Media S.A. 2021
Subjects:	municipal solid waste influencing factors machine learning deep learning SHAP value General Works A
Online Access:	https://doaj.org/article/16e5941a33dd4c76bb2a1ff9e04b30a6
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id	oai:doaj.org-article:16e5941a33dd4c76bb2a1ff9e04b30a6
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spelling	oai:doaj.org-article:16e5941a33dd4c76bb2a1ff9e04b30a62021-11-08T07:31:16ZMunicipal Solid Waste Forecasting in China Based on Machine Learning Models2296-598X10.3389/fenrg.2021.763977https://doaj.org/article/16e5941a33dd4c76bb2a1ff9e04b30a62021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fenrg.2021.763977/fullhttps://doaj.org/toc/2296-598XAs the largest producing country of municipal solid waste (MSW) around the world, China is always challenged by a lower utilization rate of MSW due to a lack of a smart MSW forecasting strategy. This paper mainly aims to construct an effective MSW prediction model to handle this problem by using machine learning techniques. Based on the empirical analysis of provincial panel data from 2008 to 2019 in China, we find that the Deep Neural Network (DNN) model performs best among all machine learning models. Additionally, we introduce the SHapley Additive exPlanation (SHAP) method to unravel the correlation between MSW production and socioeconomic features (e.g., total regional GDP, population density). We also find the increase of urban population and agglomeration of wholesales and retails industries can positively promote the production of MSW in regions of high economic development, and vice versa. These results can be of help in the planning, design, and implementation of solid waste management system in China.Liping YangLiping YangYigang ZhaoXiaxia NiuZisheng SongQingxian GaoJun WuFrontiers Media S.A.articlemunicipal solid wasteinfluencing factorsmachine learningdeep learningSHAP valueGeneral WorksAENFrontiers in Energy Research, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	municipal solid waste influencing factors machine learning deep learning SHAP value General Works A
spellingShingle	municipal solid waste influencing factors machine learning deep learning SHAP value General Works A Liping Yang Liping Yang Yigang Zhao Xiaxia Niu Zisheng Song Qingxian Gao Jun Wu Municipal Solid Waste Forecasting in China Based on Machine Learning Models
description	As the largest producing country of municipal solid waste (MSW) around the world, China is always challenged by a lower utilization rate of MSW due to a lack of a smart MSW forecasting strategy. This paper mainly aims to construct an effective MSW prediction model to handle this problem by using machine learning techniques. Based on the empirical analysis of provincial panel data from 2008 to 2019 in China, we find that the Deep Neural Network (DNN) model performs best among all machine learning models. Additionally, we introduce the SHapley Additive exPlanation (SHAP) method to unravel the correlation between MSW production and socioeconomic features (e.g., total regional GDP, population density). We also find the increase of urban population and agglomeration of wholesales and retails industries can positively promote the production of MSW in regions of high economic development, and vice versa. These results can be of help in the planning, design, and implementation of solid waste management system in China.
format	article
author	Liping Yang Liping Yang Yigang Zhao Xiaxia Niu Zisheng Song Qingxian Gao Jun Wu
author_facet	Liping Yang Liping Yang Yigang Zhao Xiaxia Niu Zisheng Song Qingxian Gao Jun Wu
author_sort	Liping Yang
title	Municipal Solid Waste Forecasting in China Based on Machine Learning Models
title_short	Municipal Solid Waste Forecasting in China Based on Machine Learning Models
title_full	Municipal Solid Waste Forecasting in China Based on Machine Learning Models
title_fullStr	Municipal Solid Waste Forecasting in China Based on Machine Learning Models
title_full_unstemmed	Municipal Solid Waste Forecasting in China Based on Machine Learning Models
title_sort	municipal solid waste forecasting in china based on machine learning models
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/16e5941a33dd4c76bb2a1ff9e04b30a6
work_keys_str_mv	AT lipingyang municipalsolidwasteforecastinginchinabasedonmachinelearningmodels AT lipingyang municipalsolidwasteforecastinginchinabasedonmachinelearningmodels AT yigangzhao municipalsolidwasteforecastinginchinabasedonmachinelearningmodels AT xiaxianiu municipalsolidwasteforecastinginchinabasedonmachinelearningmodels AT zishengsong municipalsolidwasteforecastinginchinabasedonmachinelearningmodels AT qingxiangao municipalsolidwasteforecastinginchinabasedonmachinelearningmodels AT junwu municipalsolidwasteforecastinginchinabasedonmachinelearningmodels
_version_	1718442882403139584

Municipal Solid Waste Forecasting in China Based on Machine Learning Models

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