Browsing NILU by Subject "Machine learning"

Now showing items 1-7 of 7

    • Curating scientific information in knowledge infrastructures 

      Stocker, Markus; Paasonen, Pauli; Fiebig, Markus; Zaidan, Martha A; Hardisty, Alex (Journal article; Peer reviewed, 2018)
      Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental ...

    • Error Prediction of Air Quality at Monitoring Stations Using Random Forest in a Total Error Framework 

      Lepioufle, Jean-Marie; Marsteen, Leif; Johnsrud, Mona (Peer reviewed; Journal article, 2021)
      Instead of a flag valid/non-valid usually proposed in the quality control (QC) processes of air quality (AQ), we proposed a method that predicts the p-value of each observation as a value between 0 and 1. We based our error ...

    • Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines 

      Fahim, Muhammad; Sharma, Vishal; Cao, Tuan-Vu; Canberk, Berk; Duong, Trung Q. (Peer reviewed; Journal article, 2022)
      Wind turbines are one of the primary sources of renewable energy, which leads to a sustainable and efficient energy solution. It does not release any carbon emissions to pollute our planet. The wind farms monitoring and ...

    • NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for in silico nanosafety assessment 

      Afantitis, Antreas; Melagraki, Georgia; Isigonis, Panagiotis; Tsoumanis, Andreas; Varsou, Dimitra Danai; Valsami-Jones, Eugenia; Papadiamantis, Anastasios; Ellis, Laura-Jayne; Sarimveis, Haralambos; Doganis, Philip; Karatzas, Pantelis; Tsiros, Periklis; Liampa, Irene; Lobaskin, Vladimir; Greco, Dario; Serra, Angela; Kinaret, Pia Anneli Sofia; Saarimaki, Laura Aliisa; Grafström, Roland; Kohonen, Pekka; Nymark, Penny; Willighagen, Egon; Puzyn, Tomasz; Rybinska-Fryca, Anna; Lyubartsev, Alexander; Jensen, Keld Alstrup; Brandenburg, Gerit; Lofts, Stephen; Svendsen, Claus; Harrison, Samuel; Maier, Dieter; Tamm, Kaido; Janes, Jaak; Sikk, Lauri; Dusinska, Maria; Longhin, Eleonora Marte; Rundén-Pran, Elise; Mariussen, Espen; El Yamani, Naouale; Unger, Wolfgang; Radnik, Jörg; Tropsha, Alexander; Cohen, Yoram; Leszcynski, Jerzy; Hendren, Christine Ogilvie; Wiesner, Mark; Winkler, David; Suzuki, Noriyuki; Yoon, Tae Hyun; Choi, Jang-Sik; Gulumian, Mary; Lynch, Iseult (Peer reviewed; Journal article, 2020)
      Nanotechnology has enabled the discovery of a multitude of novel materials exhibiting unique physicochemical (PChem) properties compared to their bulk analogues. These properties have led to a rapidly increasing range of ...

    • On the Robustness of Field calibration for Smart air quality monitors 

      De Vito, Saverio; Esposito, Elena; Castell, Nuria; Schneider, Philipp; Bartonova, Alena (Peer reviewed; Journal article, 2020)
      The robustness of field calibrated Air Quality Multi-sensors (AQM) performances to long term and/or mobile operation is still debated. Though accuracy generally exceeds the one of laboratory calibrations models, experimental ...

    • Towards better exploitation of Satellite data for monitoring Air Quality in Norway using downscaling techniques (SAT4AQN). Final project report. 

      Stebel, Kerstin; Schneider, Philipp; Kylling, Arve; Svendby, Tove Marit (NILU report;2/2019, Research report, 2020)
      The main goal for the “Towards better exploitation of Satellite data for monitoring Air Quality in Norway using downscaling techniques” (Sat4AQN) project was to evaluate the potential of spatially downscaling satellite ...

    • Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions 

      Lopez-Aparicio, Susana; Grythe, Henrik; Vogt, Matthias; Pierce, Matthew; Vallejo, Islen (Journal article; Peer reviewed, 2018)
      In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are designed to collect essential geo-localized information to be used as input ...