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Spatial Quality Control Method for Surface Temperature Observations Based on Multiple Elements
Método de control de calidad espacial para observaciones de temperatura superficial basado en múltiples elementos
DOI:
https://doi.org/10.15446/esrj.v21n2.65185Keywords:
Surface air temperature, Quality control, Random Forest, Principal component analysis (en)Temperatura el aire de la superficie, control de calidad, bosques aleatorios, análisis de componentes principales (es)
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Quality control can effectively improve the quality of surface meteorological observations. To ensure the stability and effectiveness of a quality control model under different terrain and climate conditions, it is necessary to structure a quality control model with strong generalization ability. Algorithms such as the Random Forest provide such generalization ability. However, machine learning algorithms are slower than traditional mathematical models. Therefore, a Random Forest quality control algorithm based on the Principal Component Analysis (PCA-RF) is proposed in this paper. Fifteen target stations under different climatic and geomorphological conditions were selected and tested using observations collected four times daily at neighboring stations from 2005-2014. The results show that using PCA to analyze the elemental composition and select elements with high correlation factors, as well as applying the Random Forest algorithm, can effectively reduce the run time and keep the accuracy of the model. The training sample dependence, model prediction accuracy and error detection rate of the PCA-RF model are superior to those of the Spatial Regression method. Therefore, the PCA-RF method is a better-quality control model for the spatial quality control of multiple elements of surface air temperature observations.
El control de calidad puede mejorar efectivamente la calidad de las observaciones meteorológicas. Para asegurar la estabilidad y efectividad de un modelo de control de calidad bajo condiciones diferentes de terreno y climáticas es necesario estructurar un esquema con una fuerte habilidad de generalización. Algoritmos como el método de bosques aleatorios (del inglés Random Forest) cumplen con estas condiciones. Sin embargo, los algoritmos de maquinas de aprendizaje son más lentos que los modelos matemáticos tradicionales. En este artículo se propone un algoritmo de control de calidad tipo bosques aleatorios basado en el Análisis de Componentes Principales (PCA-RF). Se seleccionaron 15 estaciones objetivo bajo diferentes condiciones climáticas y geomorfológicas y se evaluaron con observaciones realizadas cuatro veces por día en estaciones vecinas desde 2005 hasta 2014. Los resultados muestran que usando PCA para analizar la composición elemental y seleccionar elementos con factores de correlación alta, al igual que la aplicación del algoritmo Random Forest, se puede reducir efectivamente el tiempo de ejecución y mantener la exactitud del modelo. La dependencia de la muestra de prueba, la exactitud del modelo de predicción y la tasa de detección de error del modelo PCA-RF son superiores a aquellos del método de Regresión Espacial. Por lo tanto, el método PCA-RF es un mejor modelo para el control de calidad de elementos múltiples en las observaciones superficiales de aire y temperatura.
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