Data triangulation and machine learning: a hybrid approach to fill missing climate data

Historical data in climatology is important for recognizing patterns and discovering trends. However, data gaps often occur in some weather station time series. This paper presents a framework consisting of machine learning techniques combined with triangulation methods to identify missing meteorolo...

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Bibliographic Details
Published in:Theoretical and applied climatology 2024-06, Vol.155 (6), p.5323-5336
Main Authors: Lima, Vinícius Haender C., de Arruda Pereira, Marconi
Format: Article
Language:English
Subjects:
Aquatic Pollution
Atmospheric Protection/Air Quality Control/Air Pollution
Atmospheric Sciences
Climate science
Climatic data
Climatology
Configurations
Data analysis
Earth and Environmental Science
Earth Sciences
Identification methods
Learning algorithms
Machine learning
Meteorological data
Missing data
Neural networks
Original Paper
Pattern recognition
Regions
Statistical methods
Statistical models
Temperature data
Time series
Triangulation
Waste Water Technology
Water Management
Water Pollution Control
Weather
Weather stations
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Summary:Historical data in climatology is important for recognizing patterns and discovering trends. However, data gaps often occur in some weather station time series. This paper presents a framework consisting of machine learning techniques combined with triangulation methods to identify missing meteorological data. Our approach is based on using data from neighboring weather stations as input for triangulation methods in combination with machine learning techniques. The current focus of the proposed framework is on filling missing temperature data and it was applied in ten different regions of Brazil, each with a different climatic configuration. Furthermore, a statistical study was conducted to estimate the best configuration which showed that a popular mathematical triangulation model combined with neural networks produced the most satisfactory results in predicting missing data, outperforming the results of traditional triangulation methods.
ISSN:0177-798X
1434-4483
DOI:10.1007/s00704-024-04939-1