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

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.

Potential Use of Untreated Wastewater for Assessing COVID-19 Trends in Southern Italy

“…To standardize the different units of measurement of the four independent parameters, the data were normalized using the following Equation (3) [ 30 ]: x normalized = (x−x min)/(x max−x min) where x is each of the four variables indicated above; x max: max value of each variable; x min: min value of each variable.…”

Machine Learning and Artificial Intelligence in Circular Economy: A Bibliometric Analysis and Systematic Literature Review

“…Another crucial factor in CE is waste management for accomplishing better resource management and more waste prevention. Most of the articles in this category focus on municipal solid waste management [24,30,82,83] and liquid waste [74,81]. However, according to Ellen Macarthur Foundation, the potential value of AI in improving the design of food and electronics waste is jointly equivalent to almost 217 billion a year in 20303.…”

“…Accurately forecasting waste generation can help managing waste generation at the first place, reducing the amount of waste and finally to recycle. Fasano et al [82] did almost identical types of work. They built deep learning-based architectures to examine the variable that most affects the production of waste, separate collection, and management costs of municipal solid waste.…”

“…They used 102 variables, and different combinations of variables were used for predicting individual tasks. Different combination of layer setup was also a subject of their study [82]. Their study experimentally proves that these models could help efficient municipal solid waste management services from diverse perspectives.…”

“…Our study www.aetic.theiaer.org found several articles where researchers worked on improving the management of different types of waste, including solid waste [80,24], bio waste[633], liquid waste [74,81] and e-waste [68] with the help of different AI techniques. Articles focusing only on municipal waste management were also found [24,30,82,83]. For instance, Rosecky' et al [30] investigated several factors that impact municipal waste generation at different territories like the regional, micro-regional or municipal area in the Czech Republic.…”