Internet of things and machine learning‐based approaches in the urban solid waste management: Trends, challenges, and future directions

Abstract Solid waste management (SWM) is a crucial management entity in urban cities to handle the waste from its generation to disposal to accomplish a clean environment. The waste management operation mainly encompasses various climatic, demographic, environmental, legislative, technological, and socioeconomic dimensions. The traditional approaches deliver limitations in the process of predict and optimizing such composite non‐linear operations. The integration of the internet of things (IoT) and artificial intelligence (AI) methods have progressively gained attention by delivering potential alternatives for resolving the difficulties in SWM. This article presents a review of the significance of the amalgamation of IoT and machine learning (ML) in the SWM to predict waste generation, waste classification, route optimization, estimation of methane emissions, and so forth. The article covers the application of each ML model for the activities, including SWM, compositing, incineration, pyrolysis, gasification, landfill, and anaerobic digestion. Moreover, it is concluded that the decision tree and random forest (DT‐RF) algorithm is minor implemented, and artificial neural network (ANN) is implemented majorly in the SWM. The large number of data sets covered in the publication are secured and hidden; it limits replicating the AI models; this is also one key constraint of the non‐implementation of AI models in SWM. Scarcity of data, accurate data, rare availability of customized AI models for tackling the activities in SWM are the limitations identified from the previous studies. Implementation of low‐power ML processors, edge and fog computing‐based devices is the future direction for overcoming SWM limitations.