ORCID Profile
0000-0002-3483-1641
Current Organisation
Shahid Beheshti University
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Publisher: Springer Science and Business Media LLC
Date: 19-12-2019
Publisher: Elsevier BV
Date: 11-2016
Publisher: Elsevier BV
Date: 08-2015
Publisher: Wiley
Date: 12-03-2021
DOI: 10.1002/EP.13629
Abstract: Modeling the methane emission is challenging due to the heterogeneity of solid waste characteristics and different chemical and physical reactions leading to methane generation. This study focused on monitoring the methane generation from landfills and modeling methane emission using machine learning techniques. Hence, two pilot landfills were constructed with a total capacity of 9327 tons of municipal solid waste. The temperature, methane, and leachate generation from the pilot landfills were measured for 3 years. The effect of leachate recirculation system on methane emission from landfill was evaluated, and the results showed that the methane emission was 35% lower when leachate recirculation system was not utilized in the landfilling process. Three machine learning models, including artificial neural networks, adaptive neuro‐fuzzy inference system, and support vector machine, were used for the first time to predict methane generation. Results demonstrated that the support vector machine model was superior to both the adaptive neuro‐fuzzy inference system and artificial neural network models for predicting methane generation. The support vector machine model was able to capture 90% and 82% of the variation in methane emission from landfills with and without leachate recirculation, respectively. In general, machine learning models showed considerable potential for forecasting methane generation.
Publisher: Wiley
Date: 10-01-2013
DOI: 10.1002/EP.11747
Publisher: Elsevier BV
Date: 08-2009
Publisher: Wiley
Date: 25-10-2018
DOI: 10.1002/EP.13033
Publisher: Eurowaste SRL
Date: 2018
Publisher: Elsevier BV
Date: 10-2016
DOI: 10.1016/J.WASMAN.2016.05.018
Abstract: Municipal solid waste (MSW) management is a major concern to local governments to protect human health, the environment and to preserve natural resources. The design and operation of an effective MSW management system requires accurate estimation of future waste generation quantities. The main objective of this study was to develop a model for accurate forecasting of MSW generation that helps waste related organizations to better design and operate effective MSW management systems. Four intelligent system algorithms including support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and k-nearest neighbours (kNN) were tested for their ability to predict monthly waste generation in the Logan City Council region in Queensland, Australia. Results showed artificial intelligence models have good prediction performance and could be successfully applied to establish municipal solid waste forecasting models. Using machine learning algorithms can reliably predict monthly MSW generation by training with waste generation time series. In addition, results suggest that ANFIS system produced the most accurate forecasts of the peaks while kNN was successful in predicting the monthly averages of waste quantities. Based on the results, the total annual MSW generated in Logan City will reach 9.4×10(7)kg by 2020 while the peak monthly waste will reach 9.37×10(6)kg.
No related grants have been discovered for Maryam Abbasi.