Nussbaum, Madlene

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Number of items: 8.

Journal Article

Garosi, Younes; Ayoubi, Shamsollah; Nussbaum, Madlene; Sheklabadi, Mohsen (2022). Effects of different sources and spatial resolutions of environmental covariates on predicting soil organic carbon using machine learning in a semi-arid region of Iran Geoderma Regional, 29, e00513. Elsevier 10.1016/j.geodrs.2022.e00513

Baltensweiler, Andri; Walthert, Lorenz; Zimmermann, Stephan; Nussbaum, Madlene (2022). Hochauflösende Bodenkarten für den ­Schweizer Wald Schweizerische Zeitschrift fur Forstwesen, 173(6), pp. 288-291. Schweizerischer Forstverein 10.3188/szf.2022.0288

Meyer, Hanna; Nussbaum, Madlene; Bossew, Peter; Petremann, Eric (2021). Mapping the geogenic radon potential for Germany by machine learning Science of The Total Environment, 754, p. 142291. Elsevier 10.1016/j.scitotenv.2020.142291

Baltensweiler, Andri; Walthert, Lorenz; Hanewinkel, Marc; Zimmermann, Stephan; Nussbaum, Madlene (2021). Machine learning based soil maps for a wide range of soil properties for the forested area of Switzerland Geoderma Regional, 27(13), e00437. Elsevier 10.1016/j.geodrs.2021.e00437

Deluz, Cédric; Nussbaum, Madlene; Sauzet, Ophélie; Gondret, Karine; Boivin, Pascal (2020). Evaluation of the Potential for Soil Organic Carbon Content Monitoring With Farmers Frontiers in Environmental Science, 8(113) Frontiers Media 10.3389/fenvs.2020.00113

Greiner, Lucie; Nussbaum, Madlene; Papritz, Andreas; Fraefel, Marielle; Zimmermann, Stefan; Schwab, Peter; Grêt-Regamey, Adrienne; Keller, Armin (2018). Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau Geoderma Regional, 14, e00181. 10.1016/j.geodrs.2018.e00181

Hengl, Tomislav; Nussbaum, Madlene; Wright, Marvin N.; Heuvelink, Gerard B.M.; Gräler, Benedikt (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables PeerJ, 6, e5518. 10.7717/peerj.5518

Book Section

Nussbaum, Madlene (2022). Machine learning and processing of large data (In Press). In: Reference Module in Earth Systems and Environmental Sciences. Amsterdam, Netherlands: Elsevier 10.1016/B978-0-12-822974-3.00065-3

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