Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm op...

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Autores principales:	Nehal Elshaboury, Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, Ghasan Alfalah
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	predictive modelling trend analysis municipal solid waste particle swarm optimization hybrid neural network Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/88967927a829464e969614f7d1dae9a6
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spelling	oai:doaj.org-article:88967927a829464e969614f7d1dae9a62021-11-25T18:51:44ZPredictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model10.3390/pr91120452227-9717https://doaj.org/article/88967927a829464e969614f7d1dae9a62021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/2045https://doaj.org/toc/2227-9717Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The <i>p</i>-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.Nehal ElshabouryEslam Mohammed AbdelkaderAbobakr Al-SakkafGhasan AlfalahMDPI AGarticlepredictive modellingtrend analysismunicipal solid wasteparticle swarm optimizationhybrid neural networkChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 2045, p 2045 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	predictive modelling trend analysis municipal solid waste particle swarm optimization hybrid neural network Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	predictive modelling trend analysis municipal solid waste particle swarm optimization hybrid neural network Chemical technology TP1-1185 Chemistry QD1-999 Nehal Elshaboury Eslam Mohammed Abdelkader Abobakr Al-Sakkaf Ghasan Alfalah Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model
description	Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The <i>p</i>-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.
format	article
author	Nehal Elshaboury Eslam Mohammed Abdelkader Abobakr Al-Sakkaf Ghasan Alfalah
author_facet	Nehal Elshaboury Eslam Mohammed Abdelkader Abobakr Al-Sakkaf Ghasan Alfalah
author_sort	Nehal Elshaboury
title	Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model
title_short	Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model
title_full	Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model
title_fullStr	Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model
title_full_unstemmed	Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model
title_sort	predictive analysis of municipal solid waste generation using an optimized neural network model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/88967927a829464e969614f7d1dae9a6
work_keys_str_mv	AT nehalelshaboury predictiveanalysisofmunicipalsolidwastegenerationusinganoptimizedneuralnetworkmodel AT eslammohammedabdelkader predictiveanalysisofmunicipalsolidwastegenerationusinganoptimizedneuralnetworkmodel AT abobakralsakkaf predictiveanalysisofmunicipalsolidwastegenerationusinganoptimizedneuralnetworkmodel AT ghasanalfalah predictiveanalysisofmunicipalsolidwastegenerationusinganoptimizedneuralnetworkmodel
_version_	1718410600164360192

Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model

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