Assessing Waste Management Using Machine Learning Forecasting for Sustainable Development Goal Driven

Nada Alhathlaul (), Abderrahim Lakhouit (), Ghassan M. T. Abdalla, Abdulaziz Alghamdi, Mahmoud Shaban, Ahmed Alshahir, Shahr Alshahr, Ibtisam Alali and Fahad Mutlaq Alshammari
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Nada Alhathlaul: Department of Chemistry, College of Science, Jouf University, Sakaka 72388, Saudi Arabia
Abderrahim Lakhouit: Civil Engineering Department, University of Tabuk, Tabuk 71491, Saudi Arabia
Ghassan M. T. Abdalla: Electrical Engineering Department, University of Tabuk, Tabuk 71491, Saudi Arabia
Abdulaziz Alghamdi: Civil Engineering Department, University of Tabuk, Tabuk 71491, Saudi Arabia
Mahmoud Shaban: Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia
Ahmed Alshahir: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Shahr Alshahr: Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Ibtisam Alali: Department of Chemistry, College of Science, Jouf University, Sakaka 72388, Saudi Arabia
Fahad Mutlaq Alshammari: Department of Biology, College of Science, Jouf University, Sakaka 72388, Saudi Arabia

Sustainability, 2025, vol. 17, issue 19, 1-21

Abstract: Accurate forecasting of waste is essential for effective management and allocation of resources. As urban populations grow, the demand for municipal waste systems increases, creating the need for reliable forecasting methods to support planning and decision making. This study compares statistical models Error Trend Seasonality (ETS) and Auto Regressive Integrated Moving Average (ARIMA) with advanced machine learning approaches, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks. Five waste categories were analyzed: dead animal, building, commercial, domestic, and liquid waste. Historical datasets were used for model training and validation, with accuracy assessed through mean absolute error and root mean squared error. Results indicate that ARIMA generally outperforms ETS in forecasting building, commercial, and domestic waste streams, especially in capturing long-term domestic waste patterns. Both statistical models, however, show limitations in predicting liquid waste due to its irregular and highly variable nature, where even baseline models sometimes perform competitively. In contrast, machine learning methods consistently achieve the lowest forecasting errors across all categories. Their capacity to capture nonlinear relationships and adapt to complex datasets highlights their reliability for real-world waste management. The findings underline the importance of selecting forecasting techniques tailored to the characteristics of each waste type rather than applying a uniform method. By improving forecasting accuracy, municipalities and policymakers can design more effective waste management strategies that align with Sustainable Development Goal 11 on sustainable cities and communities, Sustainable Development Goal 12 on responsible consumption and production, and Sustainable Development Goal 13 on climate action.

Keywords: waste forecasting; waste management; resource optimization; machine learning; sustainable development goal (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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