Online machine learning for collaborative biophysical modelling. (December 2019)

• Record Type: Journal Article
• Authors: Padarian, J.; Minasny, B.; McBratney, A.B.
• Is Part Of: Environmental modelling & software. Volume 122(2019)
• Publication Date: 2019-12
• Subjects: Data privacy -- Digital soil mapping -- Neural networks; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.104548
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Many initiatives try to integrate data from different parties to solve problems that could not be addressed by a sole participant. Despite the well-known benefits of collaboration, concerns of data privacy and confidentiality are still an obstacle that impedes progress in collaborative global research. This work tackles this issue using an online-learning algorithm to generate a single model where the data remains with each party and there is no need to integrate it to a single source. This approach is demonstrated in building a global soil organic carbon model based on databases of field observations held by 65 different countries. The model is trained by visiting each country, one at a time. Only knowledge and parameters of the model are transferred between countries. The results show that it is possible that the proposed approach yields a similar prediction accuracy compared with a model that is trained with all the data. Highlights: The proposed method uses data held by multiple parties without sharing it. This type of approach is critical to promote global collaboration and ensure data privacy. The proposed approach yields similar accuracy compared with a model trained with all the data simultaneously.