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Table 3 Distinctive ANN models considered in the preliminary test

From: Coupling dynamical and statistical downscaling for high-resolution rainfall forecasting: case study of the Red River Delta, Vietnam

Model Architecturea Transfer Training data Training set dimension
function
M1n 16-10-5-3-1 LSb slp, t2, hgt, pblh 4 NVsd × 4 grids
M1a 4-4-4-4-1 LS slp, t2, hgt, pblh 4 AVse
M1s 4-4-4-4-1 LS slp, t2, hgt, pblh 4 SVsf
M2n 20-12-8-5-1 LS Rd1, slp, t2, hgt, pblh 5 NVs × 4 grids
M2a 5-5-5-5-1 LS Rd1, slp, t2, hgt, pblh 5 AVs
M2s 5-5-5-5-1 LS Rd1, slp, t2, hgt, pblh 5 SVs
M2d 20-12-8-5-1 HTSc Rd1, slp, t2, hgt, pblh 5 NVs × 4 grids
M2e 20-20-10-5-1 HTS Rd1, slp, t2, hgt, pblh 5 NVs × 4 grids
M3n 28-20-10-7-1 HTS slp, tk, hgt, pblh, u10, v10, grdflx 7 NVs × 4 grids
M4n 32-25-20-10-1 HTS Rd1, tk, hgt, slp, grdflx, psfc, pblh, q2 8 NV × 4 grids
M4a 8-6-5-5-1 HTS Rd1, tk, hgt, slp, grdflx, psfc, pblh, q2 8 AVs
M4as 16-10-5-3-1 HTS Rd1, tk, hgt, slp, grdflx, psfc, pblh, q2 8 (AVs + SVs)
M5n 32-25-20-10-1 HTS Same as M4n but for rainfall events
  1. aModel architecture indicates the number of neuron in each layer of a 5-layer MLP network, wherein first number is neurons of input, the following three numbers are neurons of hidden layers, and the last one is neurons of the output layer
  2. bLS logistic sigmoid
  3. cHTS hyperbolic tangent sigmoid
  4. d,e,fNV, AV, and SV correspond to normal variable, averaged variable, and standard deviation variable, respectively; in which:
  5. -NVs are directly extracted from the 4-predictor grids, so the actual number of variable are multiplied to 4;
  6. -AVs are the new features created by computing the mean of each variable in the 4-predictor grids;
  7. -SVs are the new features created by computing the standard deviation of each variable in the 4-predictor grids