Harnessing machine learning for advanced hazardous waste prediction

Hazardous waste (HW) poses significant risks to human health and the environment, necessitating thorough analysis and data collection. This study investigates HW data from the Gulf Cooperation Council (GCC) countries between 2010 and 2020, emphasizing key findings and the utilization of Response Surface Methodology (RSM) for precise estimation. By integrating historical data with advanced predictive modeling techniques, RSM proves to be a valuable tool for forecasting waste generation patterns and facilitating targeted resource allocation. The study underscores the importance of these algorithms in predicting waste trends, aiding authorities in identifying critical areas for intervention. The incorporation of RSM yields promising results, with R2-scores of 0.92, 0.73, and 0.93 for total hazardous wastes, medical wastes, and industrial wastes, respectively, demonstrating the effectiveness of RSM in waste management practices. By presenting these significant findings, this study contributes to better understanding and management of HW streams. To the best of our knowledge, this work represents the first attempt to employ machine-learning techniques in evaluating HW.