Can Local Geographically Restricted Measurements Be Used to Recover Missing Geo-Spatial Data?

• Main Authors: Hrvoje Kalinić, Zvonimir Bilokapić, Frano Matić
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Sensors
• Subjects: data reconstruction; machine learning; neural networks; missing data; spatio/temporal resolution; interpolation
• Online Access: https://www.mdpi.com/1424-8220/21/10/3507

The experiments conducted on the wind data provided by the European Centre for Medium-range Weather Forecasts show that 1% of the data is sufficient to reconstruct the other 99% with an average amplitude error of less than 0.5 m/s and an average angular error of less than 5 degrees. In a nutshell, our method provides an approach where a portion of the data is used as a proxy to estimate the measurements over the entire domain based only on a few measurements. In our study, we compare several machine learning techniques, namely: linear regression, K-nearest neighbours, decision trees and a neural network, and investigate the impact of sensor placement on the quality of the reconstruction. While methods provide comparable results the results show that sensor placement plays an important role. Thus, we propose that intelligent location selection for sensor placement can be done using k-means, and show that this indeed leads to increase in accuracy as compared to random sensor placement.