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Modelling of raindrop size distribution using optimized kernel fuzzy c-means clustering algorithm

Guardado en:
Publicado en:Theoretical and Applied Climatology vol. 156, no. 1 (Jan 2025), p. 47
Publicado:
Springer Nature B.V.
Materias:
Datasets
Drop size
Drop size distribution
Raindrops
Normal distribution
Measurement techniques
Size distribution
Fuzzy sets
Clustering
Rain
Chi-square test
Climate change
Data points
Raindrop size distribution
Probabilistic models
Precipitation
Hydrologic cycle
Algorithms
Performance assessment
Environmental
Acceso en línea:Citation/Abstract
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Resumen:The Drop Size Distribution (DSD) has been modelled, and the dataset is being fitted using exponential, gamma, and lognormal distribution approaches. The existing Gaussian Mixture Model (GMM) produces the best results, but it ignores or fails to focus on some data points; thus, there is still potential for improvement in the current models. To address these issues, the Optimized Kernel Fuzzy C Means clustering (KFCM) approach is used to effectively cluster data points and predict the drop size distribution. To assess the performance of the proposed model, the Chi-square test is used with rain data from various seasons and types. The results of the proposed model outperformed 11% on seasonal data, whereas the improvement of 30% to 60% is obtained in the case of rain droplets compared to the previous models.
ISSN:0177-798X
1434-4483
0344-7812
0066-6424
0376-1622
DOI:10.1007/s00704-024-05292-z
Fuente:Science Database