Forecast Municipal Solid Waste Generation in Sri Lanka

Dissanayaka, D.M.S.H.; Vasanthapriyan, Shanmuganathan

doi:10.1109/icac49085.2019.9103421

Cited by 20 publications

(18 citation statements)

References 9 publications

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“…Missing values and noise were prevalent in the current study. Linear interpolation or mean value substitution is a common solution for data completion ( Birgen et al, 2021 ; Cubillos, 2020 ; Dissanayaka and Vasanthapriyan, 2019 ). However, these methods can easily lose information.…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms in municipal solid waste management: A mini review

et al. 2021

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Population growth and the acceleration of urbanization have led to a sharp increase in municipal solid waste production, and researchers have sought to use advanced technology to solve this problem. Machine learning (ML) algorithms are good at modeling complex nonlinear processes and have been gradually adopted to promote municipal solid waste management (MSWM) and help the sustainable development of the environment in the past few years. In this study, more than 200 publications published over the last two decades (2000–2020) were reviewed and analyzed. This paper summarizes the application of ML algorithms in the whole process of MSWM, from waste generation to collection and transportation, to final disposal. Through this comprehensive review, the gaps and future directions of ML application in MSWM are discussed, providing theoretical and practical guidance for follow-up related research.

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Section: Discussionmentioning

confidence: 99%

Application of machine learning algorithms in municipal solid waste management: A mini review

et al. 2021

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“…Furthermore, most studies estimating MW quantity have not included the most substantial input factors, which may be critical information for an effective medical waste management system. Most research for estimating MW quantity has relied on surveys and questionnaires because of the absence of a historical MW database, particularly in developing nations, although this may result in inaccurate projections due to the lack of actual data [3,9,14,18,23,24]. At this point, MW prediction has problems such as limited data and many parameters affecting the amount of MW, so there is a need for more powerful algorithms, such as ensemble methods based on ML algorithms, to handle these problems.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey

Rouyendegh

Devrim-İçtenbaş

2022

Mathematics

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Predicting medical waste (MW) properly is vital for an effective waste management system (WMS), but it is difficult because of inadequate data and various factors that impact MW. This study’s primary objective was to develop an ensemble voting regression algorithm based on machine learning (ML) algorithms such as random forests (RFs), gradient boosting machines (GBMs), and adaptive boosting (AdaBoost) to predict the MW for Istanbul, the largest city in Turkey. This was the first study to use ML algorithms to predict MW, to our knowledge. First, three ML algorithms were developed based on official data. To compare their performances, performance measures such as mean absolute deviation (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R-squared) were calculated. Among the standalone ML models, RF achieved the best performance. Then, these base models were used to construct the proposed ensemble voting regression (VR) model utilizing weighted averages according to the base models’ performances. The proposed model outperformed three baseline models, with the lowest RMSE (843.70). This study gives an effective tool to practitioners and decision-makers for planning and constructing medical waste management systems by predicting the MW quantity.

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“…The hybrid ANN-GA model was proved to be the most accurate among the six models because it yielded the lowest RMSE (95.7) and the highest R 2 (0.87) and WI (0.864) values. Dissanayaka and Vasanthapriyan [30] developed a prediction model for forecasting future MSW generation in Sri Lanka using nonlinear, linear, and machine learning approaches. The correlation among the relevant factors was evaluated using principal component analysis and Pearson correlation.…”

Section: Introductionmentioning

confidence: 99%

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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Forecast Municipal Solid Waste Generation in Sri Lanka

Cited by 20 publications

References 9 publications

Application of machine learning algorithms in municipal solid waste management: A mini review

Application of machine learning algorithms in municipal solid waste management: A mini review

Ensemble Voting Regression Based on Machine Learning for Predicting Medical Waste: A Case from Turkey

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

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