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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
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
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