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Table 5 List of considered GRNN models

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

Output values

Period/normalized frequency

Set of profiles

Set of explanatory variables (site parameters)

Combinations

AF(T), F a , F v

Period

RP

6: Depth, f 0, C v , V sm, V s30, V bedrock

63 = 6 1-parameter, 15 pairs, 20 triplets, 15 quadruplets, 6 quintuplets, 1 (all 6 parameters)

AF(f/f 0)

Normalized f/f 0

RP

5: Depth, C v , V sm, V s30, V bedrock

31 = 5 1-parameter, 10 pairs, 10 triplets, 5 quadruplets 1 (all 5 parameters)

AF(T), F a , F v

Period

NP

5: Depth, f 0, C v , V sm, V s30

31 = 5 1-parameter, 10 pairs, 10 triplets, 5 quadruplets 1 (all 5 parameters)

AF(f/f 0)

Normalized f/f 0

NP

4: Depth, C v , V sm, V s30

15 = 4 1-parameter, 6 pairs, 4 triplets, 1 (all 4 parameters)

AF(T), F a , F v

Period

TP

5: Depth, f 0, C v , V sm, V s30

31 = 5 1-parameter, 10 pairs, 10 triplets, 5 quadruplets 1 (all 5 parameters)

AF(f/f 0)

Normalized f/f 0

TP

4: Depth, C v , V sm, V s30

15 = 4 1-parameter, 6 pairs, 4 triplets, 1 (all 4 parameters)