Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model

Younes, Mohammad K.; Nopiah, Zulkifli Mohd; Basri, Noor Ezlin Ahmad; Basri, Hassan; Abushammala, Mohammed F.M.; Younes, Mohammed Y.

doi:10.1016/j.wasman.2015.10.020

Cited by 51 publications

(42 citation statements)

References 29 publications

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“…While, Models "M1" (total population and literacy rate), "M2" (total population and monthly per capita consumer expenditure), "M3" (total population and per capita annual income), "M4" (total population and age group), "M8" (age group and literacy rate) and "M9" (age group and per capita annual income) predicted waste generation in the range of 350 to 400 t d -1 which is similar to KMC's prediction in connection with MSW generation. The models considering total population as input variables (M1, M2, M3 and M4) showed similar MSW generation trend for the design period, which could be hypothesized that total population is the significant governing attribute for MSW generation (Zhen-Shan et al, 2009;Younes et al, 2016).…”

Section: Resultsmentioning

confidence: 78%

Prediction of Municipal Solid Waste Generation for Developing Countries in Temporal Scale: A Fuzzy Inference System Approach

2017

Global NEST Journal

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Fuzzy Inference System (FIS) based prediction models for the Municipal Solid Waste (MSW) generation has been developed in the present work to study the influences of total population, percapita annual income, literacy rate, age group and monthly consumer expenditure on temporal variability of MSW generation for Kolhapur city, India. Ten models were developed considering two input variables at a time to study the effect of the socioeconomic and demographic parameters on MSW generation. Finally, all five input variables were considered in a single model to predict MSW generation in a temporal scale. Result shows that, the model with input variables consumer expenditure and age group was best fitted with highest coefficient of determination (0.985) value and lowest standard error of the estimate (1.562) value for the modelling period. For the design period, models related to consumer expenditure show higher waste generation. Models related to population and age show prediction similar to 'Kolhapur Municipal Corporations' prediction. However model with input literacy and income shows very low waste generation prediction. The proposed modelling technique is very useful in MSW generation prediction for a temporal scale in uncertain and random environment globally.

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Section: Resultsmentioning

confidence: 78%

Prediction of Municipal Solid Waste Generation for Developing Countries in Temporal Scale: A Fuzzy Inference System Approach

2017

Global NEST Journal

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“…Para o cálculo da área ocupada pelos resíduos sólidos urbanos gerados nos anos de 2016 a 2035, utilizou-se a Equação 1 apresentada por Younes et al (2016).…”

Section: Etapa 3 -Projeção Da áRea Ocupada Por Aterrounclassified

“…Contudo, para isso, é necessário inicialmente entender mais profundamente a atual situação dos sistemas de gestão e seus resultados. Estudos que caracterizam, diagnosticam e avaliam a atual gestão de resíduos de uma determinada região são frequentes (CHATTOPADHYAY;DUTTA;RAY, 2009;PIMENTA;CAMPOS, 2013;DICKEL et al, 2018;DINIZ;ABREU, 2018;FEITOSA;DA SILVA, 2018;GHINEA et al, 2016;GOLLAPALLI;KOTA, 2018;KUMAR;SHARMA, 2014;PIAZ;FERREIRA, 2011;YOUNES et al, 2016) e considerados essenciais para o planejamento de futuras melhorias no setor.…”

Section: Introductionunclassified

Prognóstico da gestão dos resíduos sólidos urbanos de uma cidade de médio porte: estimativa de impactos ambientais

Müller

Arruda

Alcântara

et al. 2019

Revista PO: R. Eletr. de Eng. de Produção e Correlatas

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A busca por um sistema de gestão de Resíduos Sólidos Urbanos (RSU) sustentável é um desafio para gestores municipais. Para isso, é necessário inicialmente entender o atual sistema de gestão e seus futuros resultados. Este trabalho teve o objetivo de realizar o prognóstico do atual sistema de gestão de RSU do município de Juazeiro do Norte no Ceará. O estudo é quantitativo e utilizou métodos quantitativos de modelagem para estimar, em um período de 20 anos (2016 a 2035), impactos ambientais de ocupação de área por aterro e de emissão de gás metano (CH4) e de gás carbônico (CO2). Os resultados apontam que, caso nada seja feito, os RSU coletados e enterrados no aterro do município, entre 2016 e 2035, ocuparão uma área equivalente a 20 campos de futebol (147.140 m2) e emitirão aproximadamente 153.300 e 420.600 toneladas de CH4 e CO2, respectivamente, que se estenderão por 40 anos. Os resultados dessa pesquisa destacam um cenário futuro preocupante que pode e deve ser mitigado através do planejamento e implementação de alternativas de mudanças do atual sistema de gestão de RSU da região.

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“…where A i or B j is a linguistic label (grade), such as "low" or "less", and            are the design parameters that are determined by the system developer [17]. Fig.…”

Section: Selection Of Optimum Inputs and Manfis Model Developmentmentioning

confidence: 99%

Modelling CO<sub>2</sub> and NO<sub>x</sub> on signalized roundabout using modified adaptive neural fuzzy inference system model

Sulaiman¹,

Younes

Al-Dulaimi

2017

Environmental Engineering Research

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The relative abilities of 2, dimensioned statistics -the root-mean-square error (RMSE) and the mean absolute error (MAE) -to describe average model-performance error are examined. The RMSE is of special interest because it is widely reported in the climatic and environmental literature; nevertheless, it is an inappropriate and misinterpreted measure of average error. RMSE is inappropriate because it is a function of 3 characteristics of a set of errors, rather than of one (the average error). RMSE varies with the variability within the distribution of error magnitudes and with the square root of the number of errors (n 1/2 ), as well as with the average-error magnitude (MAE). Our findings indicate that MAE is a more natural measure of average error, and (unlike RMSE) is unambiguous. Dimensioned evaluations and inter-comparisons of average model-performance error, therefore, should be based on MAE.KEY WORDS: Model-performance measures · Root-mean-square error · Mean absolute errorResale or republication not permitted without written consent of the publisher

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Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model

Cited by 51 publications

References 29 publications

Prediction of Municipal Solid Waste Generation for Developing Countries in Temporal Scale: A Fuzzy Inference System Approach

Prediction of Municipal Solid Waste Generation for Developing Countries in Temporal Scale: A Fuzzy Inference System Approach

Prognóstico da gestão dos resíduos sólidos urbanos de uma cidade de médio porte: estimativa de impactos ambientais

Modelling CO<sub>2</sub> and NO<sub>x</sub> on signalized roundabout using modified adaptive neural fuzzy inference system model

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