Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches

Kannangara, Miyuru; Dua, Rahul; Ahmadi, Leila; Bensebaa, Farid

doi:10.1016/j.wasman.2017.11.057

Cited by 237 publications

(122 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The independent variables used for modelling of ANN1/1 and ANN1/2 models were year (2013-2016) and month (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), while the output variables were the socioeconomic (ANN1/1) and waste management (ANN1/2) variables for the period from 2013 to 2016. ANN1/1 model was also used to predict the socio-economic indicators in 2017, according to a model developed using the data for a period between 2013 and 2016.…”

Section: Discussionmentioning

confidence: 99%

“…For the purpose of defining the municipal waste generation process, it is necessary to apply models and software solutions that can analyse waste streams and quantities, as well as the data on different parameters in selected area: population, economic activity, salaries, etc. On the basis of these data, it is very important to have accurate and standardized historical tracking of input data …”

Section: Introductionmentioning

confidence: 99%

“…On the basis of these data, it is very important to have accurate and standardized historical tracking of input data. 3 In some papers, 4 authors have concluded that municipal waste generation assessment should take into consideration many indicators and activities that are related to waste management, such as socio-economic parameters (demographic development, economic trends, etc.) as factors which have direct impact on waste generation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Ribić¹,

Pezo

Sinčić³

et al. 2019

Int J Energy Res

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ribić¹,

Pezo

Sinčić³

et al. 2019

Int J Energy Res

View full text Add to dashboard Cite

show abstract

“…Actualmente, existe un creciente interés tanto en países de primer mundo como en países en vías de desarrollo por aprovechar las técnicas de inteligencia artificial en predicción de la generación de RSU [7,5,6,12,4,1,9,11,13,2]. A continuación se da una breve descripción de los trabajos más recientes y que se asemejan en gran medida a la propuesta aquí planteada.…”

Section: Trabajo Relacionadounclassified

“…Mas recientemente, en [11] los autores emplean información de los censos municipales de Ontario, Canada. Las variables que los autores consideran son de información demográfica y socio-económica que describe a los municipios considerados (220 en total).…”

Section: Trabajo Relacionadounclassified

Predicción de la generación de residuos sólidos urbanos en la Ciudad de México

Calderón-Casanova¹,

López-Ortíz²,

Galán³

et al. 2018

RCS

View full text Add to dashboard Cite

show abstract

Frequency‐specific dual‐attention based adversarial network for blood oxygen level‐dependent time series prediction

Zheng,

Bao,

et al. 2024

Human Brain Mapping

View full text Add to dashboard Cite

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end‐to‐end frequency‐specific dual‐attention‐based adversarial network (FDAA‐Net) to extend the time series of existing blood oxygen level‐dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency‐dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial–temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA‐Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA‐Net effectively overcame linear frequency‐specific challenges and outperformed other popular prediction models. Test–retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA‐Net using short‐term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

show abstract

Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches

Cited by 237 publications

References 48 publications

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

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

Predicción de la generación de residuos sólidos urbanos en la Ciudad de México

Frequency‐specific dual‐attention based adversarial network for blood oxygen level‐dependent time series prediction

Contact Info

Product

Resources

About