Masoumeh Piri
1 
, Saeed Piri
2 , Hassan Taghipour
3 , Mohammad Hassan Sarbazan
4 , Mohammad Shakerkhatibi
5* 1 Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
2 Information Technology Department, Urmia University of Technology, Urmia, Iran
3 Department of Environmental Health Engineering, School of Health, Tabriz University of Medical Sciences, Tabriz, Iran
4 Department of Environment, Malayer University, Malayer, Iran
5 Health and Environment Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
Abstract
Background. Waste management is essential, for improving urban services. Predicting the quantity of waste generated can play an important role in designing and optimizing the collection, transportation, and disposal processes. In this regard, machine learning techniques have been introduced as powerful tools for predicting the quantities of generated waste. Therefore, this study was conducted with the aim of predicting the quantity of municipal solid waste (MSW) using neural networks in Tabriz city, Iran.
Methods. To predict the quantity of MSW, daily waste generation data from the Tabriz Municipality Waste Management Organization were used over a six-year period (2019–2024). The neural network employed consisted of an 8-layer LSTM network utilizing the Adam optimizer. The available data were divided into training and testing sets at an 85:15 ratio, and after the model training process, the predicted results were compared with the actual data to assess model accuracy. Root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to validate the proposed model.
Results. The results indicated good accuracy of the designed model in predicting the trend and quantity of waste generation; specifically, the RMSE, MAE, and MAPE values for the training and testing data were calculated as 50.62 and 70.47 tons/day, 32.61 and 53.02 tons/day, and 3.52% and 5.86%, respectively.
Conclusion. The analysis of the findings indicated the model's ability to reduce prediction deviation and increase reliability in estimating actual values. The low MAE values indicate that, on average, the model's prediction error compared to actual data was acceptable, and the model performed well in predicting the overall trend of waste generation. On the other hand, RMSE, which assigns greater weight to large errors, was also within a reasonable range, indicating that the model demonstrated good robustness against sudden fluctuations and outlier data. These results suggest that the trained model is capable of identifying hidden patterns in the time-series data and can be used as a suitable tool for data-driven prediction and management of municipal solid waste.
Research Insights
· Accurate prediction of municipal solid waste quantity, considering temporal fluctuations in waste generation, is essential for planning collection, processing, recycling, and disposal capacities and, ultimately, for improving sustainable waste management.
· Conventional and traditional prediction methods may have limitations in accurately representing the complex patterns, temporal fluctuations, and nonlinear relationships involved in waste generation, which may reduce their ability to accurately predict waste quantities. This limitation highlights the need for more advanced, AI-based approaches for forecasting waste generation time series. However, the use of neural networks for predicting waste quantity involves challenges, including the model’s dependence on the accuracy and validity of the data, limited access to sufficient data, and difficulty in determining an optimal network architecture. Therefore, further evaluation of neural network–based models for predicting waste quantity using real-world data is needed.
· In this study, an LSTM neural network was used to predict municipal solid waste quantity. Using daily municipal solid waste generation data from Tabriz, Iran, for the period 2019–2024, the LSTM model was trained to learn patterns in the data and predict waste quantity, and its performance was evaluated using RMSE, MAE, and MAPE.
· The findings showed that the trained model was capable of identifying complex and hidden patterns in waste data and could be used as a data-driven decision-support tool for estimating future waste quantities. These findings add to the existing evidence on the application of AI-based approaches for predicting municipal solid waste quantity, particularly using real-world data from Tabriz, and may provide a basis for future studies using longer time periods and larger datasets.
Extended Abstract
Background
In recent years, population growth, industrialization, increased urbanization, lifestyle changes, and shifts in consumption patterns have caused an increase in the quantity of municipal solid waste (MSW). Improper management of MSW constitutes a serious threat to public health and the environment, leading to pollution of water, soil, and air resources and causing various diseases in humans. Given the existing problems in managing MSW, moving toward sustainable and efficient waste management and employing novel technologies is essential. In this regard, smart technologies, and artificial intelligence in particular, have attracted attention as effective tools for transforming waste management. Accurate prediction of waste generation is one of the key components of sustainable waste management, as reliable information about waste quantities is required at various stages of reduction, collection, recycling, and disposal; such information can help prevent infrastructure failures, increase efficiency, and reduce costs. Since conventional and traditional methods are unable to accurately represent the complex, multi-factor dynamics of waste generation, the use of advanced algorithms and hybrid AI-based approaches has gained increasing attention. Considering the challenges in quantitatively assessing waste and the need to develop data-driven approaches in this field, this study aims to predict the quantity of MSW in Tabriz, to present an AI-based model, and to examine the accuracy of neural network–based algorithms.
Methods
This study was carried out in Tabriz, with a population of over 1.7 million and an area of about 324 square kilometers. Daily waste generation data were obtained from the Waste Management Organization of Tabriz Municipality for the years 2019 to 2024. First, the data were checked and corrected for missing, outlier, or invalid values to ensure their accuracy and reliability for modeling. The model used was an LSTM-based neural network with an 8-layer architecture, each layer containing 50 hidden units. To predict waste quantities, a 16-time-step input sequence from the time series was fed into the model at each step, and a learning rate of 0.001 was adopted to balance learning speed and the prevention of overfitting. The training process used the Adam optimizer and was run for 600 epochs to ensure that the model reached satisfactory convergence. The model was implemented in Python using specialized libraries, including Pandas and NumPy for data processing, Matplotlib for data visualization, and the PyTorch framework for developing and training the LSTM network. These measures made it possible to design a stable and efficient model capable of extracting hidden patterns for data-driven waste management through time-series prediction. The RMSE, MAE, and MAPE metrics were used to evaluate the model's performance and accuracy.
Results
Based on the available data, the average daily MSW generation in Tabriz was approximately 980 tons. The daily tonnage data for waste were examined, and 11 implausible records were removed; these records were replaced using a 20-day moving average based on the 10 days preceding and the 10 days following each removed record. Model performance was evaluated in two phases. In the first phase, data from 2019 to 2024 were examined, with 85% of the data used for training and the remaining 15% used for testing. The RMSE and MAE values in this phase for the training and testing data were calculated as 52.58 and 72.91 tons/day, and 33.94 and 55.01 tons/day, respectively. In the second phase, data from 2024 were also incorporated into the model; similarly, 85% of the data from 2019 to 2024 were used for training and 15% for testing. The RMSE, MAE, and MAPE values for the training and testing data were 50.62 and 70.47 tons/day, 32.61 and 53.02 tons/day, and 3.52% and 5.86%, respectively. The low MAE values indicate that the model's prediction errors were small compared to the actual data and that the model performed well in predicting the overall trend of waste generation. The RMSE values were also within a reasonable range, indicating that the model demonstrated adequate robustness against sudden fluctuations and outliers. The low MAPE values likewise indicate acceptable results.
Conclusion
This study developed an LSTM-based model to predict the quantity of MSW in Tabriz. The results show that neural networks can identify complex patterns, fluctuations, and nonlinear relationships in waste generation. Nevertheless, challenges remain in using neural networks to predict waste quantities. Such challenges include the model's dependence on the accuracy and validity of the data, limited data availability, and difficulty in determining an optimal network structure. Extending the length of the data period could improve the model's long-term predictive performance. In general, the LSTM model may serve as an effective approach for forecasting MSW generation and can assist urban managers in more precise planning for waste reduction, collection, transportation, recycling, and disposal.
Practical Implications of Research
The practical implications of this research include supporting more proactive and data-driven municipal solid waste management through the prediction of waste quantity using artificial intelligence. The application of the LSTM model can support urban decision-makers in anticipating future collection and treatment requirements, allocating resources efficiently, and improving recycling and resource recovery strategies. However, the practical implementation of the model requires the establishment of a coherent data infrastructure, continuous and standardized data collection, validation of the model under real-world conditions, and regular updating in response to changes in waste generation patterns. Overall, AI-based prediction can serve as a decision-support tool to shift municipal solid waste management from reactive decision-making toward a more data-driven, proactive, evidence-based, and sustainable approach.