| Groundwater salinity in the Horn of Africa: spatial prediction modeling and estimated people at risk
Araya, D., Podgorski, J., & Berg, M. (2023). Groundwater salinity in the Horn of Africa: spatial prediction modeling and estimated people at risk. Environment International, 176, 107925 (12 pp.). https://doi.org/10.1016/j.envint.2023.107925 |
| Long-term spatiotemporal variability of whitings in Lake Geneva from multispectral remote sensing and machine learning
Many, G., Escoffier, N., Ferrari, M., Jacquet, P., Odermatt, D., Mariethoz, G., … Perga, M. E. (2022). Long-term spatiotemporal variability of whitings in Lake Geneva from multispectral remote sensing and machine learning. Remote Sensing, 14(23), 6175 (20 pp.). https://doi.org/10.3390/rs14236175 |
| Recent ice trends in Swiss mountain lakes: 20-year analysis of MODIS imagery
Tom, M., Wu, T., Baltsavias, E., & Schindler, K. (2022). Recent ice trends in Swiss mountain lakes: 20-year analysis of MODIS imagery. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Sciencee, 90(4), 413-431. https://doi.org/10.1007/s41064-022-00215-x |
| Characterising retrieval uncertainty of chlorophyll-<em>a</em> algorithms in oligotrophic and mesotrophic lakes and reservoirs
Werther, M., Odermatt, D., Simis, S. G. H., Gurlin, D., Jorge, D. S. F., Loisel, H., … Spyrakos, E. (2022). Characterising retrieval uncertainty of chlorophyll-a algorithms in oligotrophic and mesotrophic lakes and reservoirs. ISPRS Journal of Photogrammetry and Remote Sensing, 190, 279-300. https://doi.org/10.1016/j.isprsjprs.2022.06.015 |
| Predicting chemical hazard across taxa through machine learning
Wu, J., D'Ambrosi, S., Ammann, L., Stadnicka-Michalak, J., Schirmer, K., & Baity-Jesi, M. (2022). Predicting chemical hazard across taxa through machine learning. Environment International, 163, 107184 (15 pp.). https://doi.org/10.1016/j.envint.2022.107184 |
| Machine learning for accelerating 2D flood models: potential and challenges
Jamali, B., Haghighat, E., Ignjatovic, A., Leitão, J. P., & Deletic, A. (2021). Machine learning for accelerating 2D flood models: potential and challenges. Hydrological Processes, 35(4), e14064 (14 pp.). https://doi.org/10.1002/hyp.14064 |
| Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections
Kakouei, K., Kraemer, B. M., Anneville, O., Carvalho, L., Feuchtmayr, H., Graham, J. L., … Adrian, R. (2021). Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections. Global Change Biology, 27(24), 6409-6422. https://doi.org/10.1111/gcb.15866 |
| The value of human data annotation for machine learning based anomaly detection in environmental systems
Russo, S., Besmer, M. D., Blumensaat, F., Bouffard, D., Disch, A., Hammes, F., … Villez, K. (2021). The value of human data annotation for machine learning based anomaly detection in environmental systems. Water Research, 206, 117695 (10 pp.). https://doi.org/10.1016/j.watres.2021.117695 |
| Predictive models using "cheap and easy" field measurements: can they fill a gap in planning, monitoring, and implementing fecal sludge management solutions?
Ward, B. J., Andriessen, N., Tembo, J. M., Kabika, J., Grau, M., Scheidegger, A., … Strande, L. (2021). Predictive models using "cheap and easy" field measurements: can they fill a gap in planning, monitoring, and implementing fecal sludge management solutions? Water Research, 196, 116997 (12 pp.). https://doi.org/10.1016/j.watres.2021.116997 |
| Assessing antibiotics biodegradation and effects at sub-inhibitory concentrations by quantitative microbial community deconvolution
Özel Duygan, B. D., Gaille, C., Fenner, K., & van der Meer, J. R. (2021). Assessing antibiotics biodegradation and effects at sub-inhibitory concentrations by quantitative microbial community deconvolution. Frontiers in Environmental Science, 9, 737247 (17 pp.). https://doi.org/10.3389/fenvs.2021.737247 |
| Groundwater arsenic distribution in India by machine learning geospatial modeling
Podgorski, J., Wu, R., Chakravorty, B., & Polya, D. A. (2020). Groundwater arsenic distribution in India by machine learning geospatial modeling. International Journal of Environmental Research and Public Health, 17(19), 7119 (17 pp.). https://doi.org/10.3390/ijerph17197119 |
| Active learning for anomaly detection in environmental data
Russo, S., Lürig, M., Hao, W., Matthews, B., & Villez, K. (2020). Active learning for anomaly detection in environmental data. Environmental Modelling and Software, 134, 104869 (11 pp.). https://doi.org/10.1016/j.envsoft.2020.104869 |
| Comparing dynamics: deep neural networks versus glassy systems
Baity-Jesi, M., Sagun, L., Geiger, M., Spigler, S., Ben Arous, G., Cammarota, C., … Biroli, G. (2019). Comparing dynamics: deep neural networks versus glassy systems. Journal of Statistical Mechanics, 2019(12), 124013 (15 pp.). https://doi.org/10.1088/1742-5468/ab3281 |
| How to make ecological models useful for environmental management
Schuwirth, N., Borgwardt, F., Domisch, S., Friedrichs, M., Kattwinkel, M., Kneis, D., … Vermeiren, P. (2019). How to make ecological models useful for environmental management. Ecological Modelling, 411, 108784 (14 pp.). https://doi.org/10.1016/j.ecolmodel.2019.108784 |
| Memristors for the curious outsiders
Caravelli, F., & Carbajal, J. P. (2018). Memristors for the curious outsiders. Technologies, 6(4), 118 (42 pp.). https://doi.org/10.3390/technologies6040118 |
| Robust quantification of riverine land cover dynamics by high-resolution remote sensing
Milani, G., Volpi, M., Tonolla, D., Doering, M., Robinson, C., Kneubühler, M., & Schaepman, M. (2018). Robust quantification of riverine land cover dynamics by high-resolution remote sensing. Remote Sensing of Environment, 217, 491-505. https://doi.org/10.1016/j.rse.2018.08.035 |
| Nonlinear higher order abiotic interactions explain riverine biodiversity
Ryo, M., Harvey, E., Robinson, C. T., & Altermatt, F. (2018). Nonlinear higher order abiotic interactions explain riverine biodiversity. Journal of Biogeography, 45, 628-639. https://doi.org/10.1111/jbi.13164 |
| The predictability of a lake phytoplankton community, over time-scales of hours to years
Thomas, M. K., Fontana, S., Reyes, M., Kehoe, M., & Pomati, F. (2018). The predictability of a lake phytoplankton community, over time-scales of hours to years. Ecology Letters, 21(5), 619-628. https://doi.org/10.1111/ele.12927 |
