Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

Lin, Kunsen; Zhao, Youcai; Tian, Long; Zhao, Chunlong; Zhang, Meilan; Zhou, Tao

doi:10.1016/j.scitotenv.2021.148088

Cited by 43 publications

(17 citation statements)

References 21 publications

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“…Abbasi and El Hanandeh [ 34 ] considered the ANN model for an 18-year period of data from Logan City, Australia. The potentiality of predicting the waste amount in Shanghai China using deep-learning methods has been quantified using the prediction accuracy measured by Lin et al [ 5 ], and the result indicates the correlation coefficient of attention, 1D CNN and LSTM. Vu et al [ 35 ] have considered 36 scenarios with ANN model revealed the changes in travel distance compared to the non-modified composition.…”

Section: Related Studymentioning

confidence: 99%

“…Proper and efficient forecasting is very much critical to generating an efficient system infrastructure for smart waste management. Moreover, optimized forecasting is essential to evade the spectacle of inadequacies of waste management and aid policymakers in generating advanced measures to minimize complications [ 5 ]. Many barriers exist to the implementation of digital waste management systems, including a lack of policymakers’ knowledge as well as the deficiency of standards and strategic rules [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning is a popular method incorporating advanced technologies including big data and breakthroughs in training methods [ 23 , 24 ]. The Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) are typical deep-learning algorithms [ 5 ]. CNNs are broadly used to identify objects from image and image segmentation [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

Ahmed

Mubarak

et al. 2022

IJERPH

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The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R2) and Root Mean Squared Error (RMSE). For different numbers of epochs, hidden layers, dense layers, and different units in hidden layers, the RSME values measured for 1D CNN, LSTM, GRU and Bi-LSTM models are 1.12, 1.57, 1.69 and 1.54, respectively. The best MAPE value is 1.855, which is found for the LSTM model. Therefore, our findings indicate that LSTM can be used for bin emptiness or fullness prediction for improved planning and management due to its proven resilience and increased forecast accuracy.

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Section: Related Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

Ahmed

Mubarak

et al. 2022

IJERPH

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“…In addition, the maximum normalization is extremely susceptible to extreme values. Therefore, maximum normalization is more suitable for processing boundary data [29].…”

Section: Carbon Emission Modeling Processmentioning

confidence: 99%

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Huafang

Cheng

2021

Land

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This study aimed to respond to the national “carbon peak” mid-and long-term policy plan, comprehensively promote energy conservation and emission reduction, and accurately manage and predict carbon emissions. Firstly, the proposed method analyzes the Yangtze River Economic Belt as well as its “carbon peak” and carbon emissions. Secondly, a support vector regression (SVR) machine prediction model is proposed for the carbon emission information prediction of the Yangtze River Economic Zone. This experiment uses a long short-term memory neural network (LSTM) to train the model and realize the experiment’s prediction of carbon emissions. Finally, this study obtained the fitting results of the prediction model and the training model, as well as the prediction results of the prediction model. Information indicators such as the scale of industry investment, labor efficiency output, and carbon emission intensity that affect carbon emissions in the “Yangtze River Economic Belt” basin can be used to accurately predict the carbon emissions information under this model. Therefore, the experiment shows that the SVR model for solving complex nonlinear problems can achieve a relatively excellent prediction effect under the training of LSTM. The deep learning model adopted herein realized the accurate prediction of carbon emission information in the Yangtze River Economic Zone and expanded the application space of deep learning. It provides a reference for the model in related fields of carbon emission information prediction, which has certain reference significance.

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“…Chen and Chang ( 2000 ) proposed a hybrid forecasting approach based on grey theory, fuzzy theory, and system dynamics (GFM) and validated the model’s efficiency using solid waste production 1985 to 1998 time-series data from Tainan, China, finding that the GFM method prediction accuracy was substantially higher than the single GM model. Taking 1990 to 2018 Shanghai MSW data as an example, Lin et al ( 2021 ) selected 25 generation factors and then developed a hybrid forecasting method based on a one-dimensional CNN, LSTM, and attention mechanism to depict the highly nonlinear MSW generation system. However, these hybrid approaches have limitations.…”

Section: Introductionmentioning

confidence: 99%

Long short-term memory neural network and improved particle swarm optimization–based modeling and scenario analysis for municipal solid waste generation in Shanghai, China

Wang

Yuan

Ben

et al. 2022

Environ Sci Pollut Res

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Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing.

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Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai

Cited by 43 publications

References 21 publications

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning

The Prediction of Carbon Emission Information in Yangtze River Economic Zone by Deep Learning

Long short-term memory neural network and improved particle swarm optimization–based modeling and scenario analysis for municipal solid waste generation in Shanghai, China

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