Urbanization and population growth have resulted in a global solid waste crisis, where municipal solid waste (MSW) is expected to become 3.40 billion tons per year by 2050. Inadequate waste management and traditional treatment methods endanger the environment, causing pollution, ecosystem destruction, human health problems, and resource depletion, especially when it is stored in densely populated areas or near water or sewage systems. Complex nonlinear parameters make traditional methods difficult to model and optimize. Hence there is a need for reliable technologies for efficient waste monitoring, planning and management. In this context, artificial intelligence (AI) as well as machine learning (ML) technologies has become a powerful tool as alternative computational approaches for managing solid waste. Nowadays it is an upcoming technology and is entering the waste treatment sector due to its efficiency, speed and autonomy from human operation. Various AI and hybrid technologies have been fruitfully working in various MSW management areas, like generation, collection, transportation, treatment and final disposal of waste. Artificial neural networks, decision trees, support vector machines, and genetic algorithms are the predominantly used AI models in the field of solid waste management systems. This chapter focuses on the potential of AI in forecasting of waste generation, classification, sorting, smart bins detection, vehicle routing, and resource recovery, MSW management planning and final disposal with cost-savings and process-efficiency benefits.
