Detection of long-term effect in forecasting municipal solid waste using a long short-term memory neural network

Niu, Dongjie; Wu, Fan; Dai, Shijin; He, Sheng; Wu, Boran

doi:10.1016/j.jclepro.2020.125187

Cited by 51 publications

(25 citation statements)

References 31 publications

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“…Upper and lower boundaries derived by Interquartile Range (IQR) were used to eliminate the outliers ( Equations 1 - 3 ). Data points that were higher than the upper bound or below the lower bound were removed ( Kannangara et al 2018 ; Fallah et al 2020 ; Niu et al 2021 ). If the computed lower bound is negative, then one tonne/day is taken as the lower bound.…”

Section: Methodsmentioning

confidence: 99%

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The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

Richter

et al. 2021

Sustainable Cities and Society

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A new modeling framework is proposed to estimate mixed waste disposal rates in a Canadian capital city during the pandemic. Different Recurrent Neural Network models were developed using climatic, socio-economic, and COVID-19 related daily variables with different input lag times and study periods. It is hypothesized that the use of distinct time series and lagged inputs may improve modeling accuracy. Considering the entire 7.5-year period from Jan 2013 to Sept 2020, multi-variate weekday models were sensitive with lag times in the testing stage. It appears that the selection of input variables is more important than waste model complexity. Models applying COVID-19 related inputs generally had better performance, with average MAPE of 10.1%. The optimized lag times are however similar between the periods, with slightly longer average lag for the COVID-19 at 5.3 days. Simpler models with least input variables appear to better simulate waste disposal rates, and both ‘Temp-Hum’ (Temperature-Humidity) and ‘Temp-New Test’ (Temperature-COVID new test case) models capture the general disposal trend well, with MAPE of 10.3% and 9.4%, respectively. The benefits of the use of separated time series inputs are more apparent during the COVID-19 period, with noticeable decrease in modeling error.

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Section: Methodsmentioning

confidence: 99%

“… Wu et al (2020) , on the contrary, explored regional scale ANN models on municipal solid waste generation in China. Niu et al (2021) adopted a LSTM for MSW forecasting and obtained satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

Richter

et al. 2021

Sustainable Cities and Society

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show abstract

“…Recently, with the popularity of machine learning (ML) methods, alternative methods were put forward to forecast the quantity of generated municipal solid waste effectively (Guo et al, 2021). For instance, based on the example of Suzhou (Niu et al, 2021), constructed the long shortterm memory (LSTM) neural network, autoregressive integrated moving average (ARIMA), and traditional neural network to predict the MSW production. They found that the LSTM played a vital role in predicting MSW production but did not reveal the correlation between the production of MSW and socio-economic variables.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to other energy forecasting research topics (e.g., crude oil prices, gas consumption), MSW production is also was highly influenced by various socio-economic factors (Zhang et al, 2009;Liang et al, 2019;Huang et al, 2021a). However, previous studies neither revealed the correlation between each factor and MSW production nor identified their interaction in different socioeconomic circumstances (Kannangara et al, 2018;Niu et al, 2021;Nguyen et al, 2021). In the context of China, existing studies scarcely discussed the performances and applications of different ML methods in predicting MSW.…”

Section: Introductionmentioning

confidence: 99%

Municipal Solid Waste Forecasting in China Based on Machine Learning Models

et al. 2021

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As the largest producing country of municipal solid waste (MSW) around the world, China is always challenged by a lower utilization rate of MSW due to a lack of a smart MSW forecasting strategy. This paper mainly aims to construct an effective MSW prediction model to handle this problem by using machine learning techniques. Based on the empirical analysis of provincial panel data from 2008 to 2019 in China, we find that the Deep Neural Network (DNN) model performs best among all machine learning models. Additionally, we introduce the SHapley Additive exPlanation (SHAP) method to unravel the correlation between MSW production and socioeconomic features (e.g., total regional GDP, population density). We also find the increase of urban population and agglomeration of wholesales and retails industries can positively promote the production of MSW in regions of high economic development, and vice versa. These results can be of help in the planning, design, and implementation of solid waste management system in China.

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“…In the face of the growing urban living garbage produced by complex challenges, China's urban living garbage management invested a lot of energy and technology development, both to conform to the national policy and try to reduce solid waste landfill area, seeking the right life garbage disposal methods, so the waste incineration power generation in our country urban living garbage management plays a more and more important role, garbage output accurate prediction can meet some energy needs, and ensure the effective management of municipal solid waste, to overcome the environmental pollution. At present, research methods used in the forecast of household waste power generation are mainly divided into the traditional statistical prediction method (Lionel P. Joseph et al, 2020), time series prediction method (Wu et al, 2020), and combination prediction method (Gao S et al, 2020). The traditional statistical forecasting method is based on MSW, and the quality of combustible waste in MSW is used to calculate and predict the power generation of MSW (Ayodele T. R.…”

Section: Introductionmentioning

confidence: 99%

Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long and Short Time Memory Model

Liu

Zhang

Wang

2021

Preprint

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Accurate prediction of Municipal Solid Waste electricity generation is very important for the fine management of cities. In this study, Shanghai was taken as the research object, and six influencing factors of Municipal Solid Waste production were used as input indexes to realize the effective prediction of Municipal Solid Waste production through constructing a neural network model based on bidirectional long short term memory. At the same time, based on the predicted results and the forecast formula of MSW electricity generation, this study realized the harmless treatment of Municipal Solid Waste in Shanghai. Firstly, the economic, demographic, and social indicators related to Municipal Solid Waste were determined. Secondly, the bidirectional long and short time memory model is used to learn the features of the input indexes. Finally, the electricity generation capacity of Shanghai municipal solid waste in the next six years is predicted with the municipal solid waste electricity generation formula. The experimental results show that, firstly, the MAPE value of the bidirectional long and short time memory combination model established in this paper is 7.390, and the prediction performance of this model is better than that of the other five structural methods. Second, it is predicted that in 2025, the maximum electricity generation of Shanghai Municipal Solid Waste under the three scenarios will be 512752MkW, and the minimum electricity generation of Shanghai Municipal Solid Waste will be 260668MkW. Finally, this paper can be used as a scientific information source for environmental sustainability decision-making of domestic Municipal Solid Waste electricity generation technology.

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Detection of long-term effect in forecasting municipal solid waste using a long short-term memory neural network

Cited by 51 publications

References 31 publications

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

Municipal Solid Waste Forecasting in China Based on Machine Learning Models

Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long and Short Time Memory Model

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