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

Elshaboury, Nehal; Abdelkader, Eslam Mohammed; Al-Sakkaf, Abobakr; Alfalah, Ghasan

doi:10.3390/pr9112045

Cited by 20 publications

(7 citation statements)

References 48 publications

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“…As input data, the following were used: Population, Income per capita, Employment to population ratio, Number of enterprises registered in the region per 10,000 inhabitants, and Number of enterprises by type of business activity. The model yielded R = 0.98 [40].…”

Section: Discussionmentioning

confidence: 99%

“…The model was characterized by the determination factor R 2 = 0.995 and the mean absolute percentage error MAPE = 7.757% [39]. Elshabour et al ( 2021) developed an artificial neural network model to predict the amount of waste in Poland [40]. As input data, the following were used: Population, Income per capita, Employment to population ratio, Number of enterprises registered in the region per 10,000 inhabitants, and Number of enterprises by type of business activity.…”

Section: Discussionmentioning

confidence: 99%

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Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Kulisz,

Kujawska,

Cioch

et al. 2024

Applied Sciences

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In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Kulisz,

Kujawska,

Cioch

et al. 2024

Applied Sciences

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“…This method typically utilizes Geographic Information Systems (GIS) to de ne regional boundaries and analyzes historical usage data within those regions. To enhance the accuracy of predictions, demographic and commercial activity data within the area are also incorporated 27 . Common predictive techniques include time series analysis (such as the ARIMA model) and various types of neural networks, like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).…”

Section: Region-level Bike-sharing Demand Predictionmentioning

confidence: 99%

Research on Shared Bicycle Prediction Using Gated Graph Convolutional Networks with Multi-Feature Edge Weights

Guo,

Li,

Rou

2024

Preprint

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This study proposes an hourly demand prediction method based on a multi-feature edge-weighted gated graph convolutional network to address the imbalance in station borrowing and returning demands, as well as low station utilization in bike-sharing systems. By employing graph convolutional neural networks to capture spatial relationships between stations and utilizing gating mechanisms to integrate current and historical information, it captures the long-term dependency of time series data. Creatively, it combines three single edge-weight features—station distance, time, and correlation value—into a multi-feature edge-weighted input model graph structure, enhancing the accuracy in reflecting traveler behavior characteristics. Additionally, the study considers not only temporal and spatial factors but also incorporates traveler features as node inputs to the model. Using bike-sharing trip data from Jersey City in 2020, the study employs isolation forest algorithm for outlier detection, followed by feature dependency analysis to reveal the impact of time, space, and traveler features on demand. Moreover, it accounts for the seasonal influence on bike-sharing trips by dividing the dataset into different seasons and conducting unified research on similar seasons. Results demonstrate that the multi-feature edge-weighted gated graph convolutional neural network achieves an MAE of 0.52 and MSE of 0.906 for the spring and autumn seasons, and an MAE of 0.296 and MSE of 0.594 for the summer and winter seasons, outperforming baseline models and single-feature edge-weighted predictive performance.

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“…Similarly, Elshaboury et al [32] used the same features. Further, they proposed optimizing the ANN with an evolutionary particle swarm optimization (PSO) algorithm to forecast waste generation in the Poland cities, with a reported RMSE of 11,342.74.…”

Section: Related Workmentioning

confidence: 99%

An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation

Namoun

Tufail

Alrehaili

et al. 2022

Sensors

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With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed.

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

Cited by 20 publications

References 48 publications

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Research on Shared Bicycle Prediction Using Gated Graph Convolutional Networks with Multi-Feature Edge Weights

An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation

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