Landfills are filling up, and good luck finding neighborhoods which welcoming the building of new heaps of waste.’
But new research suggests that AI and machine learning may be able to improve waste management forecasting, especially for cities without robust resources and with incomplete data.
Two researchers from the University of Johannesburg, Dr. Olusola Olaitan Ayeleru and Mr Lanrewaju Ibrahim Fajimi, conducted the study, which was published recently in the Journal of Cleaner Production.
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Ayeleru and Fajimi used machine learning to forecast the solid municipal waste in Johannesburg in 30 year’s time using a standard notebook computer with a i7 processor. The researchers used census data from 2011 indicating population, formally employed, unemployed and the number of family units. The data was supplied by the national government agency StatsSA. They combined this with data about total annual solid municipal waste at the city’s four landfill sites, from 1996 to 2008. This data was supplied by the City of Johannesburg.
In this study, Fajimi used two kinds of machine learning to generate 30-year forecasts of total solid waste generated in the city. Both algorithms are known for accurate predictions and consistency.
The first type is artificial neural networks (ANNs). This type of model can learn by itself. The researchers used five-,10-, 20-, 30- and 40-neuron models to create five forecasts The researchers used MATLAB software, which has a robust ANN neural fitting toolbox.
The second type is called supported vector machines (SVMs). The researchers used linear, quadratic, cubic, one gaussian, medium gaussian and coarse gaussian methods in MATLAB software to create another six forecasts.
The 10-neuron model produced the best ANN forecast. Among the SVM’s the linear model produced the best forecast.
The AI bottom line
The 10-neuron model predicted that the population in the City of Johannesburg is likely to increase from 5.3 million in 2021 to 6.4 million in 2031; and to 8.4 million in 2050. In contrast, the model didn’t forecast the same increase in municipal solid waste. Instead, it forecasted an increase in total annual waste from 1.61 million tons in 2021 to 1.72 million tons in 2031; and to 1.95 million tons in 2050.
In follow-up research, Ayeleru and Fajimi are investigating how to use AI to forecast the waste types and how much income the city could generate from each of those. “The City of Johannesburg is currently doing much better in its waste management compared to other large cities on the continent. This AI forecast can help facilitate the city’s design of future waste management infrastructure,” says Ayeleru.