Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Kulisz, Monika; Kujawska, Justyna

doi:10.3390/su122310088

Cited by 22 publications

(21 citation statements)

References 20 publications

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“…These metrics were considered better than that of the principal component regression (R = 0.445 and AARE = 0.066). Furthermore, Kulisz and Kujawska [9] The significant level of the neural network models' output is evaluated using the Wilcoxon-Mann-Whitney U-test, with a significance level of 0.05. The test validates the null hypothesis, which asserts that the two rankings are equal.…”

Section: Resultsmentioning

confidence: 99%

“…The data used in this research is acquired from a previous research study in Poland [9]. The waste generation characteristics of many cities with varying social and economic aspects in 2019 are provided in Table 1.…”

Section: Case Studymentioning

confidence: 99%

“…Concerning the per capita MSW generation, Denmark and Luxembourg produced the highest rates of 844 and 791 kg per inhabitant, whilst Romania produced the lowest rate of 280 kg per inhabitant in the EU [8]. The annual household waste output in Polish cities is reported to range between 238 and 315 kg per inhabitant [9]. According to the Central Statistical Office, the amount of waste increased from 10,040,108 Mg/year to 12,752,778.17 Mg/year between 2010 and 2019 [10].…”

Section: Introductionmentioning

confidence: 99%

“…Kulisz and Kujawska [9] applied neural network modeling to forecast MSW in Poland. The MSW was divided into five categories: glass, biodegradable, paper and cardboard, plastics and metals, and miscellaneous waste.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2021

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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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Section: Resultsmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…The most dominant fraction in communal waste is biological decomposted waste [49]. The organic fraction in the waste amounts to around 48-69% [50].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Communal Waste in Slovakia from the View of Chosen Economic Indicators

et al. 2021

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Waste treatment consists of activities required to make sure that waste has the least practical impact on the environment. In Slovakia, more than 50% of waste is in storage. Waste development depends on the economic situation of the state. In Slovakia, there is economic intolerance of waste treatment due to the weaker economic situation of the inhabitants. The goal of this contribution is to study the development of waste production in Slovakia in regard to economic indexes of households with the aim of improving waste management. The goal is achieved by searching for a relation between economic indexes and households by a correlation matrix and by verification of polynomial dependence. According to the results of the statistical importance, we found similarity of the regions in chosen indexes by using of cluster analysis. By this method a sustainable economy and healthy environment is guaranteed and waste is used to produce energy.

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Automated Assessment of Municipal Solid Wastes Using a Hybrid Sine Cosine Algorithm-Based Neural Network

Elshaboury,

Al-Sakkaf,

Alfalah

et al. 2024

Lecture Notes in Civil Engineering

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Prediction of Municipal Waste Generation in Poland Using Neural Network Modeling

Cited by 22 publications

References 20 publications

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

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

Evaluation of Communal Waste in Slovakia from the View of Chosen Economic Indicators

Automated Assessment of Municipal Solid Wastes Using a Hybrid Sine Cosine Algorithm-Based Neural Network

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