Evaluation of unsupervised machine learning algorithms with climate data

Juan Sebastián Ramírez; Néstor Darío Duque Méndez

Ayuda

Evaluation of unsupervised machine learning algorithms with climate data

Ramírez, Juan Sebastián ^[1] ; Duque, Néstor Darío ^[1]
1. [1] Universidad Nacional de Colombia
  
  Universidad Nacional de Colombia
  
  Colombia
Localización: Ingeniería y desarrollo: revista de la División de Ingeniería de la Universidad del Norte, ISSN 0122-3461, Vol. 40, Nº. 2, 2022, págs. 131-165
Idioma: inglés
DOI: 10.14482/inde.40.02.622.553
Títulos paralelos:
- Evaluación de algoritmos de aprendizaje de máquina no supervisados con datos climáticos
Enlaces
- Texto completo
Resumen
- español
  Al usar datos climáticos, los investigadores tienen dificultades para determinar el algoritmo de agrupamiento y los parámetros de mejor rendimiento para procesar un conjunto de datos específico.Se realiza la evaluación de algoritmos de aprendizaje automático no supervisados K-means, K-medoids y Linkage-complete, aplicados a tres conjuntos de datos con variables climatológicas (temperatura, lluvia, humedad relativa y radiación solar), para tres estaciones meteorológicas ubicadas en el departamento de Caldas, Colombia, a diferentes alturas sobre el nivel del mar. Se definen 5 escenarios para 2, 3 y 5 clústeres para cada uno de los dos algoritmos particionados y 5 escenarios para el algoritmo jerárquico, para cada una de las estaciones meteorológicas, y aplicando una cantidad y agrupación diferente de variables para los diferentes escenarios y utilizando la distancia euclidiana, Davis-Bouldin como método de evaluación de calidad de clústeres, normalización con técnicas como transformación de rango y transformación Z, varias iteraciones del algoritmo y reducción de dimensionalidad con PCA. Además, se evalúa el costo computacional. Esta investigación puede guiar al investigador sobre ciertas decisiones en el análisis de conglomerados utilizados en datos meteorológicos, así como identificar el algoritmo y los parámetros más importantes a considerar para el mejor desempeño, de acuerdo con las condiciones y requisitos particulares.
- English
  When using climate data, researchers have difficulty determining the clustering algo-rithm and the best performing parameters for processing a specific dataset. We evaluated of the following unsupervised machine learning algorithms: K-means, K-medoids and Linkage-complete, which are applied to three datasets with clima-tological variables (temperature, rainfall, relative humidity, and solar radiation) for three meteorological stations located in the department of Caldas, Colombia, at different heights above sea level. Five scenarios are defined for 2, 3, and 5 clusters for each of the two partitioned algorithms, and five scenarios for the hierarchical algorithm, in each one of the meteorological stations. Different quantities and groupings of variables are applied for the different scenarios by using Euclidean distance. Davis-Bouldin is the applied method of quality evaluation of clusters. Normalization with techniques such as range-transformation and Z-transformation, as well as some iterations of the algorithm and reduction of dimensionality with PCA. In addition, the computatio-nal cost is evaluated. This study can guide researchers on certain decisions in cluster analysis used in meteorological data, as well as identify the most important algorithm and parameters to take into consideration for the best performance, according to particular conditions and requirements.
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