A Deep Learning Approach for Cost-Effective and Environmentally Sustainable Waste Transportation Systems in Developing Countries

Abstract

Rapid urbanization has intensified waste generation, creating significant sustainability challenges and financial burdens on cities, particularly in developing countries. Effective waste transportation and management solutions are crucial to mitigating these issues, yet prior research has largely overlooked predictive modeling of transportation costs and environmental impacts. This study proposes a deep learning-based approach that integrates Long Short-Term Memory (LSTM) networks for predicting transportation cost risks and Convolutional Neural Networks (CNNs) for assessing environmental impact severity. The LSTM model captures temporal dependencies for accurate cost forecasting, while the CNN model extracts spatial patterns from incident data to classify environmental severity. Using real-world accident data from a North African waste management company, the proposed approach incorporates sustainability criteria and aligns with ISO 14001:2015 and ISO 9001:2015 standards. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 48.77 for cost prediction, while the CNN model attained 96.41% accuracy in severity classification. These findings enable stakeholders to identify and mitigate high-cost, high-environmental-impact risks and improve resilience in waste transportation. Benchmarking against traditional machine learning models highlights the superior predictive performance of deep learning techniques. Moreover, a cost-benefit analysis confirms the economic viability and long-term advantages of AI-driven waste management strategies. These findings provide actionable insights for policymakers and industry stakeholders, facilitating data-driven decision-making to enhance the resilience and sustainability of waste transportation systems.