Machine learning-assisted solid waste life-cycle management: Applications, constrains, and future opportunities

The ever-increasing amount of solid waste generated globally has given rise to issues of environmental pollution and climate change impact associated with the resource recovery and ultimate disposal. Given the complexity of waste categories and multi-objective management needs both in developed and developing countries, it is urgent to employ innovative strategies, such as the machine learning techniques. In present study, we conduct a systematic review of the application of machine learning in the waste life-cycle management with consideration of advancement, challenges, limitations, and future directions. We found that the challenges currently faced by solid waste management systems are extremely complex, necessitating the use of knowledge in the field to constrain machine learning models to enable a deep integration between machine learning and sustainable waste management. Due to its proficiency in modeling complex nonlinear processes, particularly the great advantages in prediction and multi-objective optimization, existing studies on machine learning mainly focus on waste generation projection, collection and classification, transportation, recycling, and disposal route optimization. However, the performance of machine learning applications in the field of solid waste is hindered by limitations in data quality and quantity, as well as the insufficient interpretability of individual models. These challenges can be addressed by integrating methods such as multi-source data fusion, data augmentation techniques, and ensemble learning, along with the development of highly interpretable machine learning models. Overall, the key to achieving environmentally sound management of solid waste lies in optimizing designs and balancing multidimensional evaluation criteria.