A Comparative Analysis of Machine Learning Approaches to Gap Filling Meteorological Datasets (Results Only)

This dataset contains the results from our evaluation of methodologies for filling gaps in meterological data. Variables were chosen to represent a standard set of measurements for purposes typically performed using Weather Stations installed in urban and rural areas. There are 4 dimensions by which we measure and validate each of the gap filling models across a large set of experimental configurations: meteorological variables TargetVar (dewpoint, humidity, leaf wetness, temp); feature_set (AWS, AWS-ERA5, ERA5, ERA5_Debias, Spatial); 3 types of machine learning algorithms ML (linear regression, random forests, LightGBM) combined with 2 non-ML algorithms; and gap_length: 1, 4, 36 and 288. A total of 1,720 experiments were conducted: 1,440 machine learning experiments; 160 using a spatial algorithm and 120 experiments using ERA5. For the 3 machine learning experiments, the average result was selected for each of 10 sites for 4 gap sizes (40 results) with the 3 ML models using 3 different feature sets (120 results). Data is provided in both CSV format and as a MySQL dump. Resultsets are accompanied with the SQL expression used to generate the result.