Prediction of solid waste generation and finding the sustainable pathways in the city of Dhaka
| Autor: | Md. Mosharraf Hossain, Md. Faisal Arif, Md. Sazol Ahmmed |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Solid waste management
education.field_of_study Municipal solid waste Artificial neural network Computer science Population Public Health Environmental and Occupational Health 010501 environmental sciences Management Monitoring Policy and Law 01 natural sciences Transport engineering 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis Urbanization Hidden layer education 0105 earth and related environmental sciences |
| Zdroj: | Management of Environmental Quality: An International Journal. 31:1587-1601 |
| ISSN: | 1477-7835 |
| DOI: | 10.1108/meq-10-2019-0214 |
| Popis: | PurposeSolid waste (SW) is the result of rapid urbanization and industrialization, and is increasing day by day by the increasing number of population. This thesis paper emphasizes on the prediction of SW generation in the city of Dhaka and finding sustainable pathways for minimizing the gaps in the existing system.Design/methodology/approachIn this paper, the survey of different questionnaires of the Dhaka South City Corporation (DSCC) was conducted. The data of SW generation, for few years of each month, in the city of Dhaka were collected to develop a model named Artificial Neural Network (ANN). The ANN model was used for the accurate prediction of SW generation.FindingsAt first, by using the ANN model with the one hidden layer and changing the number of neurons of the layer different models were created and tested. Finally, according to R values (training, test, all) the structure with six neurons in the hidden layer was selected as the suitable model. Finally, six gaps were found in the existing system of solid waste management (SWM) in the DSCC. These gaps are the main barrier for the better SWM.Originality/valueThe authors propose that the best model for prediction is 12-6-3, and its training and testing results are given as 0.9972 and 0.80380, respectively. So the resulting prediction is so much close in comparison with actual data. In this paper, the opportunities of those gaps are provided for working properly and the DSCC will find the better result in the aspect of SW problem. |
| Databáze: | OpenAIRE |
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