Comparative Analysis of Artificial Intelligence Algorithms for Solid Waste Volume Prediction in Support of SISWMS Design
DOI:
https://doi.org/10.64229/xa44se31Keywords:
Artificial Intelligence, Machine Learning, Sustainable Integrated Solid Waste Management, Waste Volume Forecasting, Ibadan-North LGAAbstract
Planning and designing a Sustainable Integrated Solid Waste Management System (SISWMS), as well as the decision on treatment technique to use, management policy to implement, requires an accurate and reliable forecast of the waste volume to deal with. The traditional forecasting methods, which depend solely on the demographics and socio-economic factors with few variables, can no longer handle the multifaceted and complex nature of the modern waste management system (WMS). This study mainly aims at forecasting waste volume to plan, design, and operate a SISWMS. Five artificial intelligence (AI) algorithms: Support Vector Machines (SVM), k-Nearest Neighbour (kNN), Artificial Neural Networks (ANN), Linear Regression (LR), and Adaptive Neuro Fuzzy Inference System (ANFIS) were applied on 120 months of waste data collected from Ibadan-North Local Government Area (LGA), Oyo state, South-West Nigeria. The performance of the algorithms was compared based on their respective coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Standard Error (MSE), and Mean Absolute Error (MAE). The results showed that ANFIS produced the most accurate forecast, which put the waste volume being generated in zones of Ibadan-North LGA to be 9.0947 x 104 kg/month, and by the year 2030, the peak waste volume will be 11.573 x x104 Kg/month. The results are in consonance with recent global indices and could be used as a benchmark for SISWMS and other waste management analyses.
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