Literature DB >> 33477308

Variables Influencing per Capita Production, Separate Collection, and Costs of Municipal Solid Waste in the Apulia Region (Italy): An Experience of Deep Learning.

Fabrizio Fasano1, Anna Sabrina Addante1, Barbara Valenzano2, Giovanni Scannicchio1.   

Abstract

Municipal solid waste (MSW) must be managed to reduce its impact on environmental matrices and population health as much as possible. In particular, the variables that influence the production, separate waste collection, and costs of MSW must be understood. Although many studies have shown that such factors are specific to an area, the awareness of these factors has created opportunities to implement operations to enable more effective and efficient MSW management services, and to specifically respond to the variables that have the most impact. The deep learning approaches used in this study are effective in achieving this goal and can be used in any other territorial context to ensure that the organizations that deal with these issues are more aware and create useful plans to promote the circular economy. Our findings indicate the important influence of number of rooms in a residential buildings and construction years on MSW production, the combination of services such as municipal collection centers and door-to-door service for separate MSW collection and the characteristics of the residential buildings in the municipalities on MSW management costs.

Entities:  

Keywords:  deep learning; door to door service; municipal collection centers; municipal solid waste; separate collection; waste management

Year:  2021        PMID: 33477308      PMCID: PMC7830471          DOI: 10.3390/ijerph18020752

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


  20 in total

1.  How can I deal with missing data in my study?

Authors:  D A Bennett
Journal:  Aust N Z J Public Health       Date:  2001-10       Impact factor: 2.939

Review 2.  Multiple imputation: a primer.

Authors:  J L Schafer
Journal:  Stat Methods Med Res       Date:  1999-03       Impact factor: 3.021

3.  Assessing recycling versus incineration of key materials in municipal waste: The importance of efficient energy recovery and transport distances.

Authors:  Hanna Merrild; Anna W Larsen; Thomas H Christensen
Journal:  Waste Manag       Date:  2012-01-20       Impact factor: 7.145

4.  Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

Authors:  Sama Azadi; Ayoub Karimi-Jashni
Journal:  Waste Manag       Date:  2015-10-09       Impact factor: 7.145

Review 5.  Representation learning: a review and new perspectives.

Authors:  Yoshua Bengio; Aaron Courville; Pascal Vincent
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2013-08       Impact factor: 6.226

6.  Forecasting municipal solid waste generation using prognostic tools and regression analysis.

Authors:  Cristina Ghinea; Elena Niculina Drăgoi; Elena-Diana Comăniţă; Marius Gavrilescu; Teofil Câmpean; Silvia Curteanu; Maria Gavrilescu
Journal:  J Environ Manage       Date:  2016-07-22       Impact factor: 6.789

7.  Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis.

Authors:  Vladimir M Adamović; Davor Z Antanasijević; Mirjana Đ Ristić; Aleksandra A Perić-Grujić; Viktor V Pocajt
Journal:  Environ Sci Pollut Res Int       Date:  2016-10-07       Impact factor: 4.223

Review 8.  From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

Authors:  Lu Zhang; Jianjun Tan; Dan Han; Hao Zhu
Journal:  Drug Discov Today       Date:  2017-09-04       Impact factor: 7.851

9.  Legionella and legionellosis in touristic-recreational facilities: Influence of climate factors and geostatistical analysis in Southern Italy (2001-2017).

Authors:  Osvalda De Giglio; Fabrizio Fasano; Giusy Diella; Marco Lopuzzo; Christian Napoli; Francesca Apollonio; Silvia Brigida; Carla Calia; Carmen Campanale; Angelo Marzella; Chrysovalentinos Pousis; Serafina Rutigliano; Francesco Triggiano; Giuseppina Caggiano; Maria Teresa Montagna
Journal:  Environ Res       Date:  2019-09-06       Impact factor: 6.498

10.  Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks.

Authors:  Johanna Karina Solano Meza; David Orjuela Yepes; Javier Rodrigo-Ilarri; Eduardo Cassiraga
Journal:  Heliyon       Date:  2019-11-14
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