20.500.12556/DKUM-85976 IoT and satellite sensor data integration for assessment of environmental variables: a case study on NO2 This paper introduces a novel approach to increase the spatiotemporal resolution of an arbitrary environmental variable. This is achieved by utilizing machine learning algorithms to construct a satellite-like image at any given time moment, based on the measurements from IoT sensors. The target variables are calculated by an ensemble of regression models. The observed area is gridded, and partitioned into Voronoi cells based on the IoT sensors, whose measurements are available at the considered time. The pixels in each cell have a separate regression model, and take into account the measurements of the central and neighboring IoT sensors. The proposed approach was used to assess NO2 data, which were obtained from the Sentinel-5 Precursor satellite and IoT ground sensors. The approach was tested with three different machine learning algorithms: 1-nearest neighbor, linear regression and a feed-forward neural network. The highest accuracy yield was from the prediction models built with the feed-forward neural network, with an RMSE of 15.49 ×10−6 mol/m2. Internet of Things IoT remote sensing sensor integration machine learning ensemble method internet stvari daljinsko zazanavanje strojno učenje true false true MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/journal/sensors © 2022 by the authors Znanstveno delo 2023-09-22 14:46:54 2023-09-22 14:46:54 2025-04-01 10:13:23 0000-00-00 00:00:00 2022 0 0 16 str. Št. 15, št. članka 5660 Letn. 22 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2022-05-19 2022-07-25 2022-07-28 004.9 118032387 10.3390/s22155660 1424-8220 IoT_and_Satellite_Sensor_Data_Int-Cukjati-2022.pdf IoT_and_Satellite_Sensor_Data_Int-Cukjati-2022.pdf 1 42F71FB273A6D70746CFBA9474CBC28B bbc209b5c4ef2544878619be31be7f4ca008c7c519b84af1ea178282dcfc4c17 f066f70b-5946-11ee-944f-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=172827 https://www.mdpi.com/1424-8220/22/15/5660 0 fb1280aa-5946-11ee-944f-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=172828 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0