The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

Jahandideh, Sepideh; Jahandideh, Samad; Barzegari, Ebrahim; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

doi:10.1016/j.wasman.2009.06.027

Cited by 79 publications

(51 citation statements)

References 10 publications

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“…Lately, research activities in forecasting with ANN have indicated that it can be a promising substitute for conventional linear methods. ANN is highly attractive due to its remarkable characteristics, pertinent particularly to noise and fault tolerance, high parallelism, learning and generalisation capabilities, and nonlinearity [19][20][21][22][23].…”

Section: Principle Of Artificial Neural Network Modelmentioning

confidence: 99%

“…The smaller the value of the error indices for a specified model, the higher the prediction performance of the model [20,21].…”

Section: Model Performance Evaluationmentioning

confidence: 99%

“…To the best of the authors' knowledge, there are no reported data on the application of CCD to forecasting waste generation. ANN is a brain neuron-inspired data-driven technique that can directly learn linear and nonlinear relationships between variables from a set of data compared to the conventional forecasting techniques [20][21][22]. The strengths and weaknesses of the proposed models were elucidated and an optimal prediction model was established based on conformity with the actual dataset and sensitivity analyses.…”

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: Principle Of Artificial Neural Network Modelmentioning

confidence: 99%

“…The smaller the value of the error indices for a specified model, the higher the prediction performance of the model [20,21].…”

Section: Model Performance Evaluationmentioning

confidence: 99%

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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“…classification and analysis of segregation practice), definition of indicators and linear regression analysis of gathered data [7,10,[18][19][20]. However Jahandideh et al [21] defined the non-linear nature between the parameters on the rate of medical waste generation and use of combined method (neural networks and multiple linear regression) for prediction of waste generation rates in medical institutions. The hospitals' public data on waste generation amounts were used to validate a model.…”

Section: Methodsmentioning

confidence: 99%

Assessment Method of Health Care Waste Generation in Latvia and Kazakhstan

et al. 2015

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Effective management of health care waste in medical institutions is a crucial factor for disease transmission risks, environmental pollution and, due to high utlisation costs of health care waste comparing to general waste, also economic sustainability of medical institutions. Possibilities to forecast amount of generated waste is needed for planning of waste management budgets and optimization of waste management practices. The objective of the research is to develop a health care waste generation rates prediction model for Kazakhstan hospitals. The current research describes the concept of the research and performs comparative analysis of the indicators describing health care waste management in medical institutions.

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“…Predictions based on openly accessible socio-economic factors provides necessary data for various interested parties from public and business sectors. Such knowledge can influence the choosing to the non-linear nature of ANNs in problem solving, which provides the opportunity for relating independent variables to dependent ones non-linearly (Jahandideh et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Forecasting Hazardous Waste Generation Using Short Data Sets: Case Study of Lithuania / Pavojingųjų Atliekų Susidarymo Prognozavimas Naudojant Trumpas Duomenų Imtis: Lietuvos Atvejis

Karpušenkaitė

Денафас

Ruzgas

2016

Mokslas – Lietuvos Ateitis

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and development of effective hazardous waste utilization methods or the establishment of certain facilities.The overview of previously conducted research on waste matter in general showed that various mathematical forecasting methods were used to predict solid waste generation. Only a few published research papers that were able to be found using a wide access to scientific journals and subscribed scientific databases uncovered successful application of mathematical prognostic methods applicable for few types of hazardous waste, but not hazardous waste in general. This paper will only overview those research papers that showed the most promising forecasting results.A study conducted by Abdol et al. (2011) presented the approach to unravel the interpolating problem of various structures of artificial neural networks (ANN) for the long-term prediction of solid waste generation (SWG). Results indicate that the multilayer perception approach has more advantages in comparison with traditional methods, like MLR, in predicting the municipal SWG (Abdol et al. 2011).Artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the total rate of medical waste generation and in different types of sharp, infectious and general waste. ANNs showed high performance measure values (R 2 = 0,99 Abstract. Due to inefficient waste sorting in primary and secondary waste generation sources Lithuania fails in trying to meet EU requirements for waste management sector regarding the amount of waste flow that reaches landfills. Especially sensitive situation is with hazardous waste, which often are disposed along with municipal solid waste and with it reaches landfills and due to the fact that mechanical and biological treatment plant are only now being established in the biggest cities of Lithuania, landfills becomes a big issue. The main purpose of this research is to find out which mathematical modelling methods could be fitted and if it is possible to forecast annual hazardous waste generation by using automotive, medical and daylight lamps waste generation statistical data. This is part of a research of medical, automotive and daylight lamps waste generation forecasting possibilities. Tests on the performance of artificial neural networks, multiple linear regression, partial least squares, support vector machines and four nonparametric regression methods were conducted on two developed data sets. The best and most promising results in both cases were demonstrated by generalized additives method (R 2 = 0.99) and kernel regression (R 2 = 0.99).

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The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

Cited by 79 publications

References 10 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

Assessment Method of Health Care Waste Generation in Latvia and Kazakhstan

Forecasting Hazardous Waste Generation Using Short Data Sets: Case Study of Lithuania / Pavojingųjų Atliekų Susidarymo Prognozavimas Naudojant Trumpas Duomenų Imtis: Lietuvos Atvejis

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