Table 5 Performance metrics of different models (Lanzhou).

From: Novel hybrid data-driven modeling based on feature space reconstruction and multihead self-attention gated recurrent unit: applied to PM2.5 concentrations prediction

Dataset

Model

RMSE

/(µg/m3)

MAE

/(µg/m3)

SMAPE

/(%)

PCC

DA

MBE

Lanzhou

CNN

11.9206

7.4553

21.65%

0.6921

0.4058

1.1967

Elman

11.4293

6.5316

18.66%

0.7205

0.4746

-0.2394

LSTM

11.5811

6.9034

19.73%

0.7193

0.4167

1.2016

BiLSTM35

11.3894

6.4826

18.63%

0.7231

0.4275

-0.4265

GRU

11.6285

6.6551

18.80%

0.7148

0.4094

-0.1408

MSAGRU

11.2284

6.5533

18.94%

0.7309

0.4275

0.0151

CNN-GRU56

11.7766

7.3760

21.55%

0.6970

0.4565

0.6116

CEEMDAN-GRU

7.1517

4.2856

13.67%

0.9003

0.7174

-0.2237

STL-MSAGRU

5.2494

3.2411

9.97%

0.9475

0.6957

-0.1645

STL-LSTM

6.0625

3.3259

9.64%

0.9302

0.7428

-0.0977

STL2-LSTM

3.8915

2.4367

8.16%

0.9722

0.7790

-0.0390

3D CNN-GRU36

10.5887

6.8007

19.80%

0.7749

0.5797

1.6954

EEMD-ALSTM69

5.6154

4.0844

14.74%

0.9432

0.7246

-1.3517

FSR-MSAGRU

0.5246

0.1875

0.63%

0.9995

0.9928

-0.0936