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Table 8 Evolution of the standard deviation of residuals for various GRNN models predicting F a and F v in the RP–RF case

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

Number of parameters Explanatory parameters F a F v
Standard deviation of residuals Variance reduction Standard deviation of residuals Variance reduction
All (6) Depth + f 0 + V sm + C v  + V s30 + V bedrock 0.0020 99.9% 0.00023 99.9%
3 (best triplet) f 0 + C v  + V s30 0.0094 99.6% 0.0067 99.5%
2 (best pair) f 0 + C v 0.0349 94.9% 0.0173 97.0%
2 (convenient pair) f 0 + V s30 0.1083 50.7% 0.0515 73.2%
1 (best for Fa) C v 0.0878 67.6% 0.0786 37.6%
1 (best for Fv) f 0 0.1355 22.9% 0.0661 55.9%
1 (usual) V s30 0.1419 15.4% 0.0752 42.9%
Initial σ 0.1543 0.0995