Advances in Meteorology

Research Article

A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm

Table 2

BIC of various probability distributions for DAI data of varying time scales.
DistributionDAI-1DAI-3DAI-6DAI-9DAI-12DAI-24DAI-48
2P beta−381.6−288.4−344.4−379.5−352.1−484.3−403.2
3P Weibull−692.1−509.2−536.4−586.1−524.6−647.9−506.0
4P beta−689.3−510.4−534.1−585.8−522.5−648.4−507.0
Arcsine−342.3−368.5−400.1−439.4−403.4−541.8−455.4
Burr−311.8−309.4−350.4−384.4−356.6−489.0−407.6
Cauchy−401.7−477.7−517.7−568.2−502.0−613.6−492.8
Chi−404.2−293.2−349.2−384.1−356.6−489.0−407.6
Chi-square−526.1−436.2−473.4−384.8−359.9−623.7−408.4
Cosine−193.5−365.3−505.8−579.2−510.7−649.8−509.2
Curvilinear trapezoidal−348.7−464.7−500.5−515.8−471.9−632.8−463.5
Exponential−388.7−472.4−459.9−469.6−409.3−540.3−432.9
F−577.9−283.4−373.8−402.7−365.7−494.3−407.5
Gamma−630.8−518.3−543.1−594.6−527.9−647.7−508.4
Generalized extreme value−535.0−535.0−539.0−591.6−527.7−643.1−504.3
Generalized normal−594.4−534.4−539.0−591.3−527.7−643.1−504.1
Gumbel−373.6−501.5−542.9−595.8−531.6−646.8−507.5
Inverse chi-square−275.9−363.5−382.9−410.2−372.1−500.9−412.7
Inverse gamma−507.4−501.5−522.2−582.6−532.2−647.2−508.3
Inverse Gaussian−418.2−511.7−532.2−588.8−531.5−648.0−508.5
Johnson SB−595.1−530.0−534.6−586.8−523.3−531.9−423.9
Johnson SU−589.6−534.4−534.6−587.9−523.3−638.5−499.8
Laplace−408.6−486.4−538.9−584.6−508.3−623.5−498.9
Logistic−353.2−480.6−532.0−589.7−517.5−640.6−505.6
Log-normal−579.8−527.0−537.3−590.9−531.3−647.7−508.5
Normal−339.1−472.5−529.1−585.8−515.6−645.8−507.6
Rayleigh−352.6−485.9−537.8−589.1−517.7−651.8−510.2
Scaled/shifted t−409.2−482.4−527.5−585.4−513.2−641.1−503.2
Skewed-normal−359.5−497.7−537.8−591.2−527.3−646.2−506.4
Trapezoidal−331.9−486.9−529.0−579.5−523.9−651.4−480.4
Triangular−334.7−490.5−530.9−583.8−524.5−647.0−506.5
Uniform−250.4−288.6−353.2−482.1−361.6−547.8−408.8
von Mises−343.5−332.8−344.6−379.7−352.1−484.3−403.2