A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China

Li, Xu; Gao, Pei-Qing; Cui, Shenghui; Chun, Liu

doi:10.1016/j.wasman.2013.02.012

Cited by 70 publications

(42 citation statements)

References 23 publications

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“…Meanwhile, the process of predicting HSW generation is challenging and often intensified by uncontrollable parameters [10,13]. In recent years, various conventional, regression, non-algorithm and descriptive statistical methods of forecasting municipal solid waste (MSW) generation have been reported [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Among these methods, MLR is widely applied to forecasting waste generation due to its simple algorithm and well-developed statistical theory [15]. However, MLR can neither adapt to new situations nor learn from new data; its precision is poor when imprecise data are utilised and it rarely considers all factors affecting waste generation [12,17,18].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Azarmi

Oladipo²,

Vaziri³

et al. 2018

Sustainability

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This study was undertaken to forecast the waste generation rates of the accommodation sector in North Cyprus. Three predictor models, multiple linear regression (MLR), artificial neural networks (ANNs) and central composite design (CCD), were applied to predict the waste generation rate during the lean and peak seasons. ANN showed highest prediction performance, specifically, lowest values of the standard error of prediction (SEP = 2.153), mean absolute error (MAE = 1.378) and highest R 2 value (0.998) confirmed the accuracy of the model. The analysed waste was categorised into recyclable, general waste and food residue. The authors estimated the total waste generated during the lean season at 2010.5 kg/day, in which large hotels accounted for the largest fraction (66.7%), followed by medium-sized hotels (19.4%) and guesthouses (2.6%). During the peak season, about 49.6% increases in waste generation rates were obtained. Interestingly, 45% of the waste was generated by British tourists, while the least waste was generated by African tourists (7.5%). The ANN predicted that small and large hotels would produce 5.45 and 22.24 tons of waste by the year 2020, respectively. The findings herein are promising and useful in establishing a sustainable waste management system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Azarmi

Oladipo²,

Vaziri³

et al. 2018

Sustainability

View full text Add to dashboard Cite

show abstract

“…Time series models have also been used successfully in order to assess the seasonal variations of waste generation (Denafas et al, 2014). Other models combine autoregressive techniques with seasonal exponential smoothing (Rimaityte et al, 2012), grey system theory (Xu et al, 2013) or support vector machines (Pai et al, 2010). Data-driven models run input-output data being able to identify their relationships.…”

Section: Introductionmentioning

confidence: 99%

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay

et al. 2015

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The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The present works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.

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“…Threats arise because of the modest choices during suitability mapping (Xu et al, 2013). Numerous research are being conducted to improve the techniques and methods of landfill sites suitability mapping (Gupta et al, 2015).…”

Section: General Introductionmentioning

confidence: 99%

Spatial Data Mining Toolbox for Mapping Suitability of Landfill Sites Using Neural Networks

Abujayyab

Ahamad

Yahya

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Mapping the suitability of landfill sites is a complex field and is involved with multidiscipline. The purpose of this research is to create an ArcGIS spatial data mining toolbox for mapping the suitability of landfill sites at a regional scale using neural networks. The toolbox is constructed from six sub-tools to prepare, train, and process data. The employment of the toolbox is straightforward. The multilayer perceptron (MLP) neural networks structure with a backpropagation learning algorithm is used. The dataset is mined from the north states in Malaysia. A total of 14 criteria are utilized to build the training dataset. The toolbox provides a platform for decision makers to implement neural networks for mapping the suitability of landfill sites in the ArcGIS environment. The result shows the ability of the toolbox to produce suitability maps for landfill sites.

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A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China

Cited by 70 publications

References 23 publications

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Comparative Modelling and Artificial Neural Network Inspired Prediction of Waste Generation Rates of Hospitality Industry: The Case of North Cyprus

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay

Spatial Data Mining Toolbox for Mapping Suitability of Landfill Sites Using Neural Networks

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