Modelling municipal solid waste generation: A review

Beigl, Peter; Lebersorger, Sandra; Salhofer, Stefan

doi:10.1016/j.wasman.2006.12.011

Cited by 383 publications

(280 citation statements)

References 29 publications

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“…However, to manage and reutilize hospitality sector waste (HSW) in a sustainable way, accurate prediction of HSW generation rate and composition is important [10][11][12]. A failure to make accurate HSW predictions and assessments could lead to several widespread problems in waste management systems and the environment, including irrelevant policies, increased environmental impacts as well as inadequate or overestimated capacity of disposal infrastructures.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Azarmi

Oladipo²,

Vaziri³

et al. 2018

Sustainability

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This study was undertaken to forecast the waste generation rates of the accommodation sector in North Cyprus. Three predictor models, multiple linear regression (MLR), artificial neural networks (ANNs) and central composite design (CCD), were applied to predict the waste generation rate during the lean and peak seasons. ANN showed highest prediction performance, specifically, lowest values of the standard error of prediction (SEP = 2.153), mean absolute error (MAE = 1.378) and highest R 2 value (0.998) confirmed the accuracy of the model. The analysed waste was categorised into recyclable, general waste and food residue. The authors estimated the total waste generated during the lean season at 2010.5 kg/day, in which large hotels accounted for the largest fraction (66.7%), followed by medium-sized hotels (19.4%) and guesthouses (2.6%). During the peak season, about 49.6% increases in waste generation rates were obtained. Interestingly, 45% of the waste was generated by British tourists, while the least waste was generated by African tourists (7.5%). The ANN predicted that small and large hotels would produce 5.45 and 22.24 tons of waste by the year 2020, respectively. The findings herein are promising and useful in establishing a sustainable waste management system.

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Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the process of predicting HSW generation is challenging and often intensified by uncontrollable parameters [10,13]. In recent years, various conventional, regression, non-algorithm and descriptive statistical methods of forecasting municipal solid waste (MSW) generation have been reported [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Azarmi

Oladipo²,

Vaziri³

et al. 2018

Sustainability

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show abstract

“…• prediction of data and as an independent variables consists of GDP RM per capita (Beigl et al, 2004), population (*1000b), demand of electricity (kWh/capita) and the number of employment. Second, perform a correlation analysis.…”

Section: Proposed Conceptual Frameworkmentioning

confidence: 99%

Multilinear regression analysis on solid waste generation quantity in Malaysia towards sustainable development

Zulkipli

2017

Int. j. adv. appl. sci.

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Inadequate data will affect the efficiency of future planning of solid waste management in order to achieve sustainable development. The purpose of this paper is to investigate the effect of a number of factors, namely GDP, Demand of electricity, Population and Number of Employment, which could be applied to predict the solid waste generation quantities and improve the management of future planning. The data were statistically analyzed by conducting a bivariate analysis and multilinear regression analysis. The results revealed that the GDP, Demand of electricity, Population and Number of Employment reflects the prediction of sustainable solid waste generation. It was found that addition of all predictor variables accounted for 98.9 percent (r = 0.989) changes in the variance in the quantity of solid waste generation. Consequently, the department of solid waste can increase its effectiveness and efficiency in management through the prediction of the quantity of solid waste generation.

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“…The quantity of municipal waste strongly depends on the socio-economic conditions of the region [20]. The generation of MSW relates mainly to the nature and intensity of economic activities and the size of the population.…”

Section: Municipal Waste Generation and Compositionmentioning

confidence: 99%

The Climate Change Impact of Possible Municipal Solic Waste Management Scenarios in Estonia

2017

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Waste management has an influence on the greenhouse gas (GHG) formation. The emissions of greenhouse gases vary between the EU countries depending on waste treatment practices and other regional factors such us composition of waste. The aim of this paper was to examine, from a life-cycle perspective, Municipal Solid Waste (MSW) management in the context of greenhouse gas formation and to evaluate the possible reduction of climate change potential of alternative waste management options in Estonia. The paper summarises the results of a case study in Estonia, assessing the climate change impact by 2020 in terms of net greenhouse gas emissions from two possible management scenarios. As a result it can be concluded that better management of municipal waste and diversion of municipal waste away from landfills could significantly reduce the emissions of GHG and, if high rates of recycling and incineration with energy recovery are attained, the net greenhouse gas emissions may even become negative. It means that these waste management options can partly offset the emissions that occurred when the products were manufactured from virgin materials and energy was produced from fossil fuels. This is especially important concerning the climate change impact.

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Modelling municipal solid waste generation: A review

Cited by 383 publications

References 29 publications

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Multilinear regression analysis on solid waste generation quantity in Malaysia towards sustainable development

The Climate Change Impact of Possible Municipal Solic Waste Management Scenarios in Estonia

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