Time Series Analysis of Municipal Solid Waste Generation Rates

Katsamaki, Anastasia; Willems, Sander; Diamadopoulos, Evan

doi:10.1061/(asce)0733-9372(1998)124:2(178)

Cited by 36 publications

(14 citation statements)

References 13 publications

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“…Anastasia et el. [8] used Box-Jenkins methodology used for data analysis and stochastic modeling of daily municipal solid waste production. The data sets examined are the daily quantities of municipal solid wastes for consecutive day and for each day separately.…”

Section: Introductionmentioning

confidence: 99%

Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis

Momani¹

2009

American J. of Environmental Sciences

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Problem statement: Time series analysis and forecasting has become a major tool in different applications in hydrology and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). Approach: In this study we used Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken for Amman airport station for the period from 1922-1999 with a total of 936 readings. Results: In this research, ARIMA (1, 0, 0) (0, 1, 1)12 model was developed. This model is used to forecasting the monthly rainfall for the upcoming 10 years to help decision makers establish priorities in terms of water demand management. Conclusion/Recommendations: An intervention time series analysis could be used to forecast the peak values of rainfall data.

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Section: Introductionmentioning

confidence: 99%

Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis

Momani¹

2009

American J. of Environmental Sciences

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“…To predict waste generation, time-series regression analysis (Katsamaki et al, 1998;Navarro-Ésbri et al, 2002), system dynamics models , and other regression models have been applied (Grossman et al, 1974).…”

Section: Forecasting Modelmentioning

confidence: 99%

Solid waste management in European countries: A review of systems analysis techniques

Pires

Chang

2011

Journal of Environmental Management

423

177

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“…Ahn (1999) used an autoregressive model with covariate to predict atmospheric phosphorus deposition in South Florida, USA. Time series modelling has also been used for other environmental engineering applications, such as prediction of air pollution levels (Salcedo et al 1999) and rate estimation of municipal solid waste generation (Katsamaki et al 1998). Time series modelling has also been used for other environmental engineering applications, such as prediction of air pollution levels (Salcedo et al 1999) and rate estimation of municipal solid waste generation (Katsamaki et al 1998).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks and time series modelling of TP concentration in boreal streams: a comparative approach

Nour¹,

Smith²,

El-Din³

et al. 2006

Journal of Environmental Engineering and Science

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Increased nutrient concentrations in fresh waters may lead to depressed dissolved oxygen concentrations, increased cyanobacterial biomass, and potentially high levels of cyanobacterial toxin production. Phosphorus loading, during snow melt and storm events, is the main source of nutrient enrichment to water bodies on the Canadian Boreal Plain. This study compared two approaches for modelling total phosphorus (TP) concentration: autoregressive moving average with exogenous input (ARMAX) and artificial neural network (ANN) models. Derived models were applied to a small forested watershed on the Canadian Boreal Plain. Results showed that ANN outperformed ARMAX based on four measures of goodness-of-fit statistics. This study confirmed that the ANN modelling approach is superior to ARMAX in modelling time-correlated gapped data, provided step-by-step guidelines for modelling time-correlated variables, and presented a feasible alternative for modelling diffuse pollutants in small forested watersheds.Résumé: Laugmentation dans les concentrations de nutriments dans les eaux douces peut engendrer une diminution des concentrations en oxygène dissous, une augmentation de la biomasse de cyanobactéries et des hauts niveaux potentiels de production de toxines de cyanobactéries. Les charges de phosphore, durant la fonte des neiges et les tempêtes, sont la source principale d'enrichissement en nutriments des plans d'eau de la plaine boréale canadienne. Cette étude compare deux approches de modélisation de la concentration en phosphore total : la moyenne mobile autorégressive avec entrée exogène (ARMAX, Auto-Regressive Moving Average with exogenous inputs) et les modèles de réseaux de neurones artificiels (RNA). Des modèles dérivés ont été appliqués à un petit bassin hydrologique boisé de la plaine boréale canadienne. En se basant sur quatre mesures de statistiques de la qualité de l'ajustement, les résultats ont montré que les RNA étaient plus performants que le modèle ARMAX. La présente étude confirme que l'approche de modélisation par les RNA est supérieure au modèle ARMAX dans la modélisation des données à espacement dépendant du temps, qu'elle fournit des lignes directrices étape par étape pour la modélisation de variables temporelles et présente une alternative pratique de modélisation des polluants dispersés dans de petits bassins hydrologiques boisés.Mots clés: bassin hydrologique, fonte des neiges, polluants dispersés, phosphore, réseaux de neurones artificiels (RNA), série chronologique, couche cachée multiplaque, ARMAX, forêt boréale.[Traduit par la RédactionJ

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Time Series Analysis of Municipal Solid Waste Generation Rates

Cited by 36 publications

References 13 publications

Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis

Time Series Analysis Model for Rainfall Data in Jordan: Case Study for Using Time Series Analysis

Solid waste management in European countries: A review of systems analysis techniques

Artificial neural networks and time series modelling of TP concentration in boreal streams: a comparative approach

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