The forecasting of municipal waste generation using artificial neural networks and sustainability indicators

Antanasijević, Davor; Pocajt, Viktor; Popović, Ivanka G.; Redžić, Nebojša; Ristić, Mirjana

doi:10.1007/s11625-012-0161-9

Cited by 91 publications

(34 citation statements)

References 21 publications

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“…For complex and irregular time series, in 2012, Wu [6] introduced gene-expression programming into the forecasting of urban construction waste generation and combined the advantages of time-series and factor-regression models. In 2013, Antanasijevic et al [7] established a backpropagation neural network (BPNN) and a general regression neural network (GRNN) model for forecasting urban solid-waste generation. They discovered that the GRNN model significantly outperformed the conventional BPNN model and exhibited relatively high stability even for long-term forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

LSTM-Based Forecasting for Urban Construction Waste Generation

et al. 2020

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Accurate forecasts of construction waste are important for recycling the waste and formulating relevant governmental policies. Deficiencies in reliable forecasting methods and historical data hinder the prediction of this waste in long- or short-term planning. To effectively forecast construction waste, a time-series forecasting method is proposed in this study, based on a three-layer long short-term memory (LSTM) network and univariate time-series data with limited sample points. This method involves network structure design and implementation algorithms for network training and the forecasting process. Numerical experiments were performed with statistical construction waste data for Shanghai and Hong Kong. Compared with other time-series forecasting models such as ridge regression (RR), support vector regression (SVR), and back-propagation neural networks (BPNN), this paper demonstrates that the proposed LSTM-based forecasting model is effective and accurate in predicting construction waste generation.

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Section: Literature Reviewmentioning

confidence: 99%

LSTM-Based Forecasting for Urban Construction Waste Generation

et al. 2020

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“…Usually, the lack of adequate management resources is hidden behind the unyielding stance on ecology (O'Brien, 2013) which in turn creates urban and environmental problems (Antanasijevic et al, 2013).…”

Section: Motivation Towards Recyclingmentioning

confidence: 99%

Untitled

2019

IJBESAR

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Purpose:The purpose of this study is to determine the resilience of oil intensive Romanian communities to oil price fluctuations. Design/methodology/approach: The methodology employed is based on identifying the communities with the most extensive oil extraction activities using GIS (Geographic Information System) software. We then apply a random effects panel regression model to check the significance of the oil price as a predictor of employment. Finding:Results indicate that the effect of oil price fluctuations is limited, with employment in the identified communities following the national trend. Research limitations/implications:Unfortunately, our study is hindered by data availability issues and a short time series. Nevertheless, conclusions are backed by the country's characteristics -a diversified economy, integration with refining operations, and the presence of related manufacturing and services activities. Originality/value:The study adds to the existing literature by focusing on a mature region with a long history of hydrocarbon extraction activities. We argue that the study of local communities in mature regions is of great importance in the context of the ongoing energy transition, particularly of those located within the European Union. JEL Classifications O13, Q32, Q35Keywords: random effects, oil, resource curse, local community ©International Hellenic University powers. This is known as the Green Deal, the main goal of which is achieving climate neutrality at the EU level by 2050 (European Commission, 2019). Fossil fuels sectors active in the EU will undoubtedly be affected by these ambitions. This makes this analysis highly relevant for policymakers.In order to provide additional context, the next section offers a short history of the oil and gas industry in Romania. We then highlight the relevant literature. The following section presents the data used and explains the methodology. We next discuss the results and finally we offer conclusions.

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Section: Motivation Towards Recyclingmentioning

confidence: 99%

Citizens' Intent and Behavior Towards Recycling in the Municipality of Kavala

Kechagias¹,

Dimitriadis²

2019

IJBESAR

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Purpose:This study examines the factors that tend to affect citizens intent and behavior towards recycling. There are several key factors that can influence this behavior, such as Individual perception and behavior towards recycling as well as the spatial planning of recycling bins and the recycling culture. Design/methodology/approach: Taking these variables into account a quantitative survey was carried out on a sample of 307 people in the municipality of Kavala from June 2018 to June 2019. The data were gathered through the use of a structured questionnaire implemented with the SPSS 20.0 and the techniques applied were Correlation Analysis, Regression analysis and ANOVA analysis. Finding:The results of the survey showed that "Recycling Behavior" and "Recycling Culture" are the factors that affect the behavior towards recycling the most according to the citizens of Kavala. On the other hand "Spatial planning of recycling bins" seems to be indifferent to the participants while "Individual perception about recycling" is the least agreeable factor. Research limitations/implications:One of the most basic and uncontrolled constraints is the objectivity of the responses given by the individuals who completed the questionnaires. It is worth mentioning the negative reaction of many male respondents when they were informed about the subject of the survey. In a way, the researcher tried to obtain the most objective answers possible through clarifying questions. In addition, some of the difficulties faced by the researcher were the negative responses about answering the questionnaire using as an excuse the lack of time, while individual cases of respondents (10) refused to participate in the research because its issue was not of their concern or interest. Originality/value: This survey shows citizens' behavior towards recycling following the implementation of recycling measures by the Municipality of Kavala. JEL Classifications Q50, Q53, Q56, Q58

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The forecasting of municipal waste generation using artificial neural networks and sustainability indicators

Cited by 91 publications

References 21 publications

LSTM-Based Forecasting for Urban Construction Waste Generation

LSTM-Based Forecasting for Urban Construction Waste Generation

Untitled

Citizens' Intent and Behavior Towards Recycling in the Municipality of Kavala

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