A Multistep Chaotic Model for Municipal Solid Waste Generation Prediction

Song, Jingwei; He, Jiaying

doi:10.1089/ees.2014.0031

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…All three indices indicate that the SA based hybrid forecast outperforms the three individual models, meaning that it can be recommended for practical use. Previous report [ 15 ] has shown that chaotic model outperforms the other two models around 2%. In this study, the hybrid model raises the accuracy of chaotic model to another 1.75%.…”

Section: Resultsmentioning

confidence: 94%

“…Navarro-Esbri et al [ 5 ] proposed a nonlinear dynamics algorithm by state space reconstruction and a fitting function for this trajectory of the system. Song and He [ 15 ] proposed a chaotic model of nearest neighbor state space reconstruction for multistep ahead MSW generation prediction. These nonlinear dynamic system methods are based on the embedding theorem of Takens [ 16 ] and aim to rebuild the dynamic behavior from a discrete time series data with time delay and embedding dimension.…”

Section: Introductionmentioning

confidence: 99%

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Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

Song

Mao

et al. 2014

The Scientific World Journal

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A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%.

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Section: Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

Song

Mao

et al. 2014

The Scientific World Journal

Self Cite

View full text Add to dashboard Cite

show abstract

“…The studies published previously indicate NARX as a most often applied method (Menezes & Barreto, 2008;Shen & Chang, 2013;Singh & Satija, 2016;Younes et al, 2015) which performs the standard neural-network-based predictors (Song & He, 2014), illustrates the nonlinear data and different relationships (Younes et al, 2015) and with recurrent connection as the most important property.…”

Section: Connection Weightsmentioning

confidence: 99%

Artificial Neural Network Applied in Forecasting the Composition of Municipal Solid Waste in Iasi, Romania

Ghinea

Cozma

2021

Journal of Environmental Engineering and Landscape Management

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Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.

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“…Authors such as [11][12][13] use support vector machine regression (SVMR) with different strategies, with the objective of forecasting different chaotic series. It is determined that SVMR with multiple exits presents better results on iterative machines.…”

Section: Introductionmentioning

confidence: 99%

Forecast of Chaotic Series in a Horizon Superior to the Inverse of the Maximum Lyapunov Exponent

et al. 2018

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In this article, two models of the forecast of time series obtained from the chaotic dynamic systems are presented: the Lorenz system, the manufacture system, and the volume of the Great Salt Lake of Utah. The theory of the nonlinear dynamic systems indicates the capacity of making good-quality predictions of series coming from dynamic systems with chaotic behavior up to a temporal horizon determined by the inverse of the major Lyapunov exponent. The analysis of the Fourier power spectrum and the calculation of the maximum Lyapunov exponent allow confirming the origin of the series from a chaotic dynamic system. The delay time and the global dimension are employed as parameters in the models of forecast of artificial neuronal networks (ANN) and support vector machine (SVM). This research demonstrates how forecast models built with ANN and SVM have the capacity of making forecasts of good quality, in a superior temporal horizon at the determined interval by the inverse of the maximum Lyapunov exponent or theoretical forecast frontier before deteriorating exponentially.

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A Multistep Chaotic Model for Municipal Solid Waste Generation Prediction

Cited by 9 publications

References 30 publications

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

Artificial Neural Network Applied in Forecasting the Composition of Municipal Solid Waste in Iasi, Romania

Forecast of Chaotic Series in a Horizon Superior to the Inverse of the Maximum Lyapunov Exponent

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