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Table 9 Evolution of the standard deviation of residuals for various GRNN models predicting F a and F v in the NP–RF case

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

Normalized 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 sm + C v  + V s30

0.0055

99.74%

0.0011

99.98%

3 (best triplet)

f 0 + C v  + V s30

0.012

98.7%

0.0034

99.8%

2 (best pair for Fa)

f 0 + C v

0.0318

91.43%

0.0172

95.7%

2 (convenient pair)

f 0 + V s30

0.0592

70.28%

0.0153

96.6%

1 (best for Fa)

C v

0.0783

48.02%

0.0733

21.8%

1 (best for Fv)

f 0

0.0944

24.44%

0.048

66.4%

1 (usual)

V s30

0.0926

27.3%

0.0709

26.8%

Initial σ

0.1086

0.0829