Evaluating the capability of municipal solid waste separation in China based on AHP-EWM and BP neural network

Trentino (an Italian Province located in the northern part of the country) is equipped with a management system of municipal solid waste collection at the forefront. Among the most positive aspects, there is a great ability for waste separation at the source and a consequent low production of residual municipal solid waste for disposal. Latest data show a gross efficiency of selective collection that has recently reached 80%, one of the highest values in Italy. This study analyzed the “Trentino system” to identify the main elements that have been at the base of the current efficient model. This provided an opportunity to propose a selective collection quality index (SCQI), including collection efficiency for each fraction, method of collection, quality of the collected materials, presence of the punctual tariff and tourist incidence. A period relevant for the transition of the collection system to the recent one was chosen for the demonstrative adoption of the proposed indicators in order to determine the potential of the index adoption. Results of the analysis of this case study were obtained in a quantitative form thanks to the sub-parameters that characterize the proposed index. This allowed selected collection decision makers to focus intently on a territory to find criticalities to be solved. For instance, the use of the index and its sub-indicators in the case of Trentino identified and comparatively quantified the local problems resulting from the presence of a large museum in a small town, tourism peaks in some valleys, and a delay in the punctual tariff adoption. The index has been proposed with the aim to make available an integrated tool to analyze other areas in Italy and abroad.

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ARTIFICIAL NEURAL NETWORK APPLIED IN FORECASTING THE COMPOSITION OF MUNICIPAL SOLID WASTE IN IASI, ROMANIA

Journal of Environmental Engineering and Landscape Management ◽

10.3846/jeelm.2021.15553 ◽

2021 ◽

Vol 29 (3) ◽

pp. 368-380

Author(s):

Cristina Ghinea ◽

Petronela Cozma ◽

Maria Gavrilescu

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Root Mean Square Error ◽

Municipal Solid Waste ◽

Solid Waste ◽

Mean Square Error ◽

Coefficient Of Determination ◽

Mean Square ◽

Ann Model

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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Predictive Analysis of Municipal Solid Waste Generation Using an Optimized Neural Network Model

Processes ◽

10.3390/pr9112045 ◽

2021 ◽

Vol 9 (11) ◽

pp. 2045

Author(s):

Nehal Elshaboury ◽

Eslam Mohammed Abdelkader ◽

Ghasan Alfalah ◽

Abobakr Al-Sakkaf

Keyword(s):

Neural Network ◽

Waste Management ◽

Municipal Solid Waste ◽

Solid Waste ◽

Mean Squared Error ◽

Pearson Correlation ◽

Mean Bias Error ◽

Bias Error ◽

P Value ◽

Waste Generation

Developing successful municipal waste management planning strategies is crucial for implementing sustainable development. The research proposed the application of an optimized artificial neural network (ANN) to forecast quantities of waste in Poland. The neural network coupled with particle swarm optimization (PSO) algorithm is compared to the conventional neural network using five assessment metrics. The metrics are coefficient of efficiency (CE), Pearson correlation coefficient (R), Willmott’s index of agreement (WI), root mean squared error (RMSE), and mean bias error (MBE). Selected explanatory factors are incorporated in the developed models to reflect the influence of economic, demographic, and social aspects on the rate of waste generation. These factors are population, employment to population ratio, revenue per capita, number of entities by type of business activity, and number of entities enlisted in REGON per 10,000 population. According to the findings, the ANN–PSO model (CE = 0.92, R = 0.96, WI = 0.98, RMSE = 11,342.74, and MBE = 6548.55) significantly outperforms the traditional ANN model (CE = 0.11, R = 0.68, WI = 0.78, RMSE = 38,571.68, and MBE = 30,652.04). The significant level of the reported outputs is evaluated using the Wilcoxon–Mann–Whitney U-test, with a significance level of 0.05. The p-values of the pairings (ANN, observed) and (ANN, ANN–PSO) are all less than 0.05, suggesting that the models are statistically different. On the other hand, the P-value of (ANN–PSO, observed) is more than 0.05, suggesting that the difference between the models is statistically insignificant. Therefore, the proposed ANN–PSO model proves its efficiency at estimating municipal solid waste quantities and may be regarded as a cost-efficient method of developing integrated waste management systems.

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