Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis
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- 作者：Vladimir M. Adamović ; Davor Z. Antanasijević…
- 关键词：MSW management ; General regression neural network ; Structural breaks
- 刊名：Environmental Science and Pollution Research
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：24
- 期：1
- 页码：299-311
- 全文大小：
- 刊物类别：Earth and Environmental Science
- 刊物主题：Environment, general; Environmental Chemistry; Ecotoxicology; Environmental Health; Atmospheric Protection/Air Quality Control/Air Pollution; Waste Water Technology / Water Pollution Control / Water M
- 出版者：Springer Berlin Heidelberg
- ISSN：1614-7499
- 卷排序：24
文摘
This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000–2012) for each country was determined and confirmed using the Chow test and Quandt–Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0 % compared to 6.7 % generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.