ISSN 2394-5125
 

Research Article 


NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION

PEYMAN AJORLOO.

Abstract
This study aimed to compare neural network and regression in forecasting municipal solid waste generation. The research method used in this
article is descriptive and purpose-based. In order to identify the effective factors in forecasting waste production, due to the nature of the topic,
research questions and research objectives, first a list of variables of municipal waste production has been prepared by reviewing the research
background. Then, by using Delphi technique and survey of academic experts as well as the managers active in the waste industry, effective factors
on municipal waste production has been identified. Also by studying the research literature on methods of multivariate regression and neural
network analysis, comprehensive and complete information was collected and data collection tools of standard questionnaire related to survey to
identify effective factors are performed by using the Delphi technique. The team of experts in this study are related to waste specialists and
academics as well as university professors who have been used to survey factors affecting municipal waste production, which are a number of 15
people. After collecting the data using Delphi technique, the required variables were identified, and then were analyzed by using multivariate
regression and artificial neural network method, which MATLAB, EXCEL, and SPSS software have been used for this purpose. According to the
results, the artificial neural network model has better capability to forecast waste production and considering MAPE and MSE of both models, the
presented model using neural network in this study has better performance in forecasting waste production than linear regression.

Key words: Waste, Forecast, Artificial Neural Networks


 
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Pubmed Style

PEYMAN AJORLOO. NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION. JCR. 2020; 7(17): 3486-3490. doi:10.31838/jcr.07.17.434


Web Style

PEYMAN AJORLOO. NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION. http://www.jcreview.com/?mno=36450 [Access: August 17, 2021]. doi:10.31838/jcr.07.17.434


AMA (American Medical Association) Style

PEYMAN AJORLOO. NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION. JCR. 2020; 7(17): 3486-3490. doi:10.31838/jcr.07.17.434



Vancouver/ICMJE Style

PEYMAN AJORLOO. NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION. JCR. (2020), [cited August 17, 2021]; 7(17): 3486-3490. doi:10.31838/jcr.07.17.434



Harvard Style

PEYMAN AJORLOO (2020) NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION. JCR, 7 (17), 3486-3490. doi:10.31838/jcr.07.17.434



Turabian Style

PEYMAN AJORLOO. 2020. NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION. Journal of Critical Reviews, 7 (17), 3486-3490. doi:10.31838/jcr.07.17.434



Chicago Style

PEYMAN AJORLOO. "NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION." Journal of Critical Reviews 7 (2020), 3486-3490. doi:10.31838/jcr.07.17.434



MLA (The Modern Language Association) Style

PEYMAN AJORLOO. "NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION." Journal of Critical Reviews 7.17 (2020), 3486-3490. Print. doi:10.31838/jcr.07.17.434



APA (American Psychological Association) Style

PEYMAN AJORLOO (2020) NEURAL NETWORK AND REGRESSION COMPARISON IN FORECASTING MUNICIPAL SOLID WASTE GENERATION. Journal of Critical Reviews, 7 (17), 3486-3490. doi:10.31838/jcr.07.17.434