Table 2 The summerized ML algorithms for predicting PPV.
From: Prediction of peak particle velocity using hybrid random forest approach
Researchers | Â | No. of parameters | ML algorithm | R2 |
|---|---|---|---|---|
Qiu29 | HR, HD, B, S, Q, CL, R, BI, E, PR, Pv, VoD, DoE | 13 | (WOA-, GWO-, BO-)XGBoost | 0.9757 |
Zhang30 | Q, R, St, B, H, S, PF, HN | 8 | PSO-XGBoost | 0.968 |
Nguyen31 | N, Q, PF, St, B, R, S, H, Δt | 9 | XGBoost | 0.952 |
Amiri32 | Q, R | 2 | KNN;ANN | 0.88 |
Armaghani33 | Q, R | 2 | ANN | 0.987 |
Ghoraba34 | Q, R | 2 | ANFIS | 0.952 |
Hasanipanah35 | Q, R | 2 | CART | 0.95 |
Khandelwal36 | Q, R | 2 | SVM | 0.96 |
Khandelwal22 | Q, R | 2 | CART | 0.92 |
Arzu37 | Q, R, Sv, S, B | 5 | ANFIS | 1 |
Monjezi38 | Q, R, S, HD | 4 | ANN | 0.9493 |
Nguyen39 | B, S, f, Q, R | 5 | (SpaSO-, SalSO-,MFO-)ELM | 0.99 |
Monjezi40 | Q, R, B/S, UCS, RW, HN | 6 | MLPNN | 0.954 |
Nguyen41 | B, S, f, PF, Q, R | 6 | (MRFO-, HGS-, AO-) SONIA | 0.896 |
Yang42 | B, S, St, Q, R | 5 | FFA-SVR | 0.992 |
Zhou24 | HD, PF, S, Q, R | 5 | FS-RF | 0.903 |
Hosseini25 | HN,B,H,HR,Q,R | 6 | BH-LSTM | 0.9956 |
Fissha26 | Q, R | 2 | PSO-DRVM | 0.917 |
