Forecasting municipal solid waste quantity using arti fi cial neural network and supported vector machine techniques: A case study of Johannesburg, South Africa

Detailed prediction of the amounts of municipal solid waste (MSW) is very crucial for planning and management of MSW in a sustainable manner. Forecasting of MSW quantity is usually very challenging owing to unavailability of data in the low-income countries (LCs) and where data are available, they are often unreliable. The aim of this study is to forecast MSW generated in the City of Johannesburg (CoJ), South Africa with the projection period in continuing guesstimates by using machine learning approach. Two of machine learning algorithms namely: artificial neural network (ANN) and supported vector machine (SVM) were employed to forecast the quantity of MSW that would be generated in the CoJ. The forecast was based on historical data obtained from Statistics South Africa (STATS SA) and the projection was made up to 2050. The data pre-testing and incorporation structure was built in MATLAB simulation software to generate datasets having satisfactory information capacity and characteristic designed for modeling. From the result obtained, it was observed that machine learning algorithm is effective for the development of models for MSW forecasting. In the ANN models, the 10 neurons structure (ANN10) performed best with a determination coefficient (R2) of 99.9%, while in the SVM models, the linear model performed best with R2 of 98.6%. From the results obtained from the ANN10 model, the total amount of MSW generated per year in the City of Johannesburg is envisaged to get to 1.95 x 106 tonnes in 2050 with an average annual waste of 1.78 x 106 tonnes. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The study utilized machine learning to forecast the generation of municipal solid waste (MSW) in Johannesburg, demonstrating the effectiveness of algorithms such as artificial neural networks and supported vector machines in modeling MSW quantities.