Research Article
Application of Extreme Learning Machine Algorithm for Drought Forecasting
Table 5
Appropriate distributions with their respective BIC values for different time scales at nine meteorological stations.
| Stations | SPTI-1 | SPTI-3 | SPTI-6 | SPTI-9 | SPTI-12 | Distribution | BIC | Distribution | BIC | Distribution | BIC | Distribution | BIC | Distribution | BIC |
| Mianwali | 3P Weibull | −472.53 | 3P Weibull | −455.20 | Gamma | −535.73 | Laplace | −351.26 | Chi-square | −326.56 | Sialkot | 4P beta | −722.18 | Generalized normal | −600.22 | Gamma | −581.78 | Gumbel | −636.77 | Gumbel | −548.40 | Muzaffarabad | 4P beta | −584.52 | Rayleigh | −670.62 | Chi-square | −739.36 | Log-normal | −964.72 | Chi-square | −866.88 | Astore | 3P Weibull | −480.83 | Gen. extreme value | −558.42 | Skewed-normal | −745.38 | Rayleigh | −756.25 | Triangular | −717.03 | Chitral | 3P Weibull | −400.89 | Generalized normal | −565.65 | Triangular | −467.11 | Normal | −478.68 | Laplace | −430.01 | Multan | 3P Weibull | −317.25 | 4P beta | −390.52 | Gumbel | −417.03 | Trapezoidal | −496.07 | Skewed-normal | −355.40 | Kalat | 4P beta | −664.62 | 4P beta | −798.51 | 4P beta | −963.70 | 4P beta | −475.47 | Trapezoidal | −500.91 | Chhor | Johnson SU | −444.44 | 3P Weibull | −353.93 | 4P beta | −337.40 | Exponential | −282.64 | Laplace | −287.91 | Kohat | 3P Weibull | −622.28 | Gumbel | −466.94 | Inverse Gaussian | −590.98 | Triangular | −391.63 | Trapezoidal | −381.36 |
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