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

Azarmi, Soolmaz L.; Oladipo, Akeem Adeyemi; Vaziri, Roozbeh; Alipour, Habib

doi:10.3390/su10092965

Cited by 29 publications

(5 citation statements)

References 32 publications

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“…Azarmi et al compared three predictive models, i.e., MLR, CCD and ANN, predicting the mean waste generation rates from hotels in North Cyprus, based on the input data, including: type of waste, season, type of accommodation and waste management practice. In the course of the research, it was noted that ANN indicated the highest efficiency (standard error of prediction SEP = 2.153, mean absolute error MAE = 1.378 and R 2 = 0.998) [14]. Similar results were obtained by Azadi and Karimiashni, who compared ANN and MLR while predicting seasonal municipal solid waste generation for Fars province, Iran.…”

Section: Introductionsupporting

confidence: 70%

“…The modern waste management models found in the literature incorporate expert systems [10][11][12], evolutionary programming [13], artificial neural networks [14][15][16][17][18], multiple linear regression (MLR) [14,19,20] central composite design (CCD) [14] and various combination of these tools. It is, however, artificial neural network (ANN) modeling that has been on the forefront due to its distinct advantages over other methods, such as the clear network model, uncomplicated implementation and the quality of performance [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Kulisz

Kujawska

2020

Sustainability

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Planning is a crucial component of short- and long-term municipal waste management. Establishing the relationships between the factors that determine the amount of waste generated by municipalities and forecasting the waste management needs plays a fundamental role in the development of effective planning strategies and implementation of sustainable development. Artificial Neural Network employed for verifying the forecasts pertaining to the amount of rainfall in Poland were presented in the studies. The proposed models included selected explanatory indices in order to reflect the impact of social, demographic and economic factors on the amount of generated waste. Mean squared error (MSE) and regression value (R) are used as indices of efficiency of the developed models. The ANN models exhibited high accuracy of forecasts at high R values (R = 0.914, R = 0.989) and low MSE values. Derived from the socioeconomic data for 2003–2019, the model predicts that the future waste generation in 2024 will increase by 2%. The results indicate that the employed ANN models are effective in predicting the amount of waste and can be considered a cost-effective approach to planning integrated waste management systems.

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Section: Introductionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Kulisz

Kujawska

2020

Sustainability

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“…Accommodation sector in particular needs to adapt themselves and adopt new strategies as well as utilizing new methods and trends. It was estimated that the amount of total waste generated by hotels during the lean season amounted to 2010.5 kg/day in north Cyprus, in which the share of large hotels was (66.7%), followed by medium size hotels (19.4%), and guesthouses (2.6%) [22]. Therefore, this study adheres to tackling the issue of sustainability which resonates with statement that: "The United Nations Sustainable Development Goals of the 2030 Agenda for Sustainable Development indicate the need to rethink the current economic growth ideology in the context of social and environmental needs in development" [2] (p. 3).…”

Section: Introductionmentioning

confidence: 99%

Investigating Sustainable Practices in Hotel Industry-from Employees’ Perspective: Evidence from a Mediterranean Island

2019

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Although several studies have researched the hotel employees’ environmental behavior, none has addressed the hotel employees’ perception of their respective hotels’ sustainability practices. This study aims to investigate the sustainable practices in four and five star hotels in a Mediterranean island by employing Global Sustainable Tourism Council (GSTC) hotel criteria indicators, indicators of sustainable development for tourism destinations (WTO), and the European Union’s (EU) sustainability framework for the Mediterranean hotels− “Nearly Zero-Energy Hotels” (NEZEH), and global sustainable development goals (SDG) in the context of three dimensions: social, economic and environment. The sampled hotels claim that their operation system is conformed to sustainability principles with the aim of furthering their green agenda. In this study, we aim to investigate the validity and extent of this claim. About 290 (N = 290) employees in the specified hotels were surveyed. The measurement instruments were compiled based on sustainability indicators that encompassed addressing social, economic, and environmental dimensions. The research questions contextualized around four main themes: effective sustainability planning, maximizing social and economic benefits for the local community, enhancing cultural heritage, and reducing negative environmental impacts. For the statistical and data analysis, SEM (structural equation modeling) is used. Study revealed that employees are a legitimate and credible source of information about sustainability practices. It is also revealed that as going green is becoming a means toward branding, hotels are making efforts to implement a genuine sustainability practice. Study also indicated that the majority of employees validated the sustainability practices as genuine.

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“…As can be seen, most of the studies have been carried out using the Singe Regression Analysis (SRA) approach, however, ANN, FL, and Multiple Regression Analysis (MRA) and are also quite commonly used. It can be noted that ANN-based models in the case of MSW management are used from different perspectives: ro analyze MSW related data [27] or capture disposal trend [28], to develop predictive models together with other machine learning (ML) or to optimization algorithms [29], deep learning approaches [30] for forecasting waste generation [31] and classification tasks based on image recognition [32].…”

Section: Introductionmentioning

confidence: 99%

ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania

Raudonis

Paulauskaitė-Tarasevičienė

Eidimtas³

2022

Sustainability

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Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year.

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

Cited by 29 publications

References 32 publications

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Investigating Sustainable Practices in Hotel Industry-from Employees’ Perspective: Evidence from a Mediterranean Island

ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania

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