Table 3 Experiments La Puebla.

From: Artificial intelligence for streamflow prediction in river basins: a use case in Mar Menor

Horizon

Algorithm

MAE

RMSE

CVRMSE

WI

NSE

1H

DT

0.013

0.134

206.498

0.804

\(-0.182\)

GB

0.011

0.067

103.727

0.827

0.702

KNN

0.012

0.042

65.334

0.810

0.882

LR

0.017

0.100

154.280

0.746

0.340

RF

0.008

0.035

53.294

0.872

0.921

L2

0.011

0.044

67.856

0.819

0.872

XGB

0.015

0.058

89.770

0.746

0.777

LSTM

0.017 ± 0.003

0.033 ± 0.003

50.673 ± 3.878

0.741 ± 0.038

0.929 ± 0.011

LSTM_IPIP

0.014 ± 0.003

0.038 ± 0.005

59.045 ± 7.008

0.784 ± 0.034

0.902 ± 0.024

12H

DT

0.074

0.383

589.773

0.165

− 8.633

GB

0.062

0.187

287.690

0.216

− 1.292

KNN

0.057

0.326

502.114

0.335

− 5.982

LR

0.058

0.149

230.323

0.217

− 0.469

RF

0.068

0.229

353.007

0.198

− 2.451

L2

0.058

0.142

219.594

0.221

− 0.336

XGB

0.078

0.214

330.679

0.197

− 2.028

LSTM

0.028 ± 0.002

0.108 ± 0.005

165.773 ± 8.374

0.545 ± 0.012

0.236 ± 0.074

LSTM_IPIP

0.028 ± 0.003

0.104 ± 0.005

159.818 ± 7.251

0.548 ± 0.015

0.290 ± 0.064

24H

DT

1.956

4.258

6565.067

0.008

− 1191.702

GB

1.148

2.287

3526.053

0.014

− 343.058

KNN

0.319

0.942

1452.420

0.056

− 57.376

LR

NC

NC

NC

NC

NC

RF

1.212

2.499

3852.896

0.011

− 409.798

L2

0.039

0.124

190.880

0.297

− 0.008

XGB

0.458

0.468

720.896

0.046

− 13.381

LSTM

0.029 ± 0.001

0.118 ± 0.001

180.525 ± 0.873

0.494 ± 0.007

0.093 ± 0.009

LSTM_IPIP

0.028 ± 0.001

0.116 ± 0.001

177.702 ± 1.736

0.484 ± 0.022

0.121 ± 0.017

  1. Significant values are in bold.