Transformer-based enhanced model for accurate prediction and comprehensive analysis of hazardous waste generation in Shanghai: Implications for sustainable waste management strategies

Abstract

The escalating generation of hazardous waste (HW) has become a pressing concern worldwide, straining waste management systems and posing significant health hazards. Addressing this challenge necessitates an accurate understanding of HW generation, which can be achieved through the application of advanced models. The Transformer model, known for its ability to capture complex nonlinear processes, proves invaluable in extracting essential features and making precise HW generation predictions. To enhance comprehension of the key factors influencing HW generation, visualization techniques such as SHapley Additive exPlanations (SHAP) provide insightful explanations. In this study, a novel approach combining classical deep learning algorithms with the Transformer model is proposed, yielding impressive results with an R2 value of 0.953 and an RMSE of 7.284 for HW prediction. Notably, among the five key fields considered—demographics, socio-economics, industrial production, environmental governance, and medical health—industrial production emerges as the primary contributor, accounting for over 50% of HW generation. Moreover, a high rate of industrial development is anticipated to further accelerate this process.