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Table 10 Evolution of the standard deviation of residuals for various GRNN models predicting F a and F v in the TP–RF case

From: Deriving amplification factors from simple site parameters using generalized regression neural networks: implications for relevant site proxies

Truncated profiles

F a

F v

Number of parameters

Explanatory parameters

Standard deviation of residuals

Variance reduction

Standard deviation of residuals

Variance reduction

All 5

Depth + f 0 + V smn + C v  + V s30

0.0095

99.28%

0.0017

99.97%

3 (best triplet)

f 0 + C v  + V s30

0.0185

97.2%

0.0061

99.57%

2 (best pair for Fa)

f 0 + C v

0.0375

88.8%

0.0229

93.9%

2 (convenient pair)

f 0 + V s30

0.0511

79.1%

0.0127

98.1%

1 (best, Fa)

C v

0.0734

57.05%

0.0736

37.9%

1 (best, Fv)

f 0

0.0764

53.47%

0.0428

79%

1 (usual)

V s30

0.0925

31.4%

0.0777

30.8%

Initial σ

0.112

0.0934