Table 1 Summary of machine learning applications for predicting MSWG.
From: Advanced predictive modeling of municipal solid waste management using robust machine learning
Author(s) | Year | ML technique(s) | Result | Best ML method |
|---|---|---|---|---|
Alidoust et al. | 2021 | ANN, MARS, MGGP, M5Tree | ANN achieved highest accuracy, R2 = 0.9812 (testing) | ANN |
Xia et al. | 2022 | Support Vector Machine (SVM) | High accuracy with R2 = 0.98; outperformed traditional methods | SVM |
Ali et al. | 2021 | Linear Regression (LR), Multilayer Perceptron (MLP), SMO | MLP achieved highest correlation coefficient 0.7169 | MLP |
Adeleke et al. | 2023 | ANFIS optimized with PSO and GA | PSO-ANFIS-FCM best model; RMSE = 2.864 predicting organic waste | PSO-ANFIS -FCM |
Singh and Uppaluri | 2023 | Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB) | GB best with RMSE = 3.01, MAE = 2.86, R2 = 0.99 | Gradient Boosting (GB) |
Nourani et al. | 2025 | FFNN, LSTM with MI and SHAP feature selection | FFNN performed well in Austin (DC = 0.7226 train, 0.6529 test); less accurate elsewhere | FFNN with MI-SHA |
