Predictive model for municipal waste generation using artificial neural networks—Case study City of Zagreb, Croatia

Abstract

Summary The European Union's environmental legislation related to environmental protection, already implemented in the national legislation of the Republic of Croatia, aims to introduce a system of integrated and sustainable waste management. Within such a system, it is of utmost importance to have a better estimate of the amount of municipal waste generated, which directly influences future planning in the waste management sector. The aim of this research was to develop and optimize models for the estimation of generated municipal waste by application of methodology using neural network models, and taking into account the socio‐economic impact as well as the inputs regarding the actual waste management trends. In this paper, an artificial neural network models were used to predict the municipal waste generation in Zagreb, Croatia. The standardized socio‐economic and waste management variables were chosen to encompass 2013 to 2016 period. Moreover, the test prediction of the observed data was performed for 2017. Developed models sufficiently predicted the quantities of different municipal waste fractions and in that sense can contribute to better planning of upcoming waste management systems that will be sustainable and in order to meet the European Union commitments.

Prediction of the Production of Separated Municipal Solid Waste by Artificial Neural Networks in Croatia and the European Union

“…Given that the mechanism of MSW generation is a very complex process and that there is a connection between socioeconomic factors and the generation of MSW, nonlinear regression models show greater accuracy than linear ones. Therefore, in recent times, the use of ANN in the prediction of waste generation is increasingly common, which also show better results [16]. Because of the above, no other mathematical models were used in this research, but only ANN.…”

“…Furthermore, so far, many researchers have successfully applied ANN in MSW forecasting in their local area, and most MSW forecasting models are based on data from a specific region or data for a specific city. Thus, ANNs were also used to estimate the production of MSW in the city of Zagreb [16]. In the aforementioned research, a mathematical model was developed for estimating the production of MSW for the period from 2013 to 2016.…”

“…The principles of the waste hierarchy recommend prioritizing from the most desirable waste prevention option at the top, to disposal as the least desirable option at the bottom. In this way, the waste hierarchy helps to shift waste as a problem to waste as a resource and, at the same time minimize the impact of waste on the environment and health to improve resource efficiency [12][13][14][15][16][17][18]. In line with the pyramid of the waste hierarchy, the long-term goal of EU policy is to reduce the amount of waste generated and, where unavoidable, to promote it as a resource by achieving higher recycling rates.…”

An optimization‐based analysis of waste to energy options for different income level countries

“…WtE technologies and their application to treat MSW have been considered in the literature before 4,23‐25 . Kumar and Samadder 26 present a review of several WtE technologies and provide an assessment of various options for different income level countries.…”

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

“…The predicted hydrogen production was in close agreement with the observed values from the experimental runs. Also Ribic et al 24 employed a MLP ANN to model municipal waste generation. The study revealed that the ANN model was robust in predicting municipal waste generation.…”