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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
Combatting over-specialization bias in growing chemical databases
Dost, K., Pullar-Strecker, Z., Brydon, L., Zhang, K., Hafner, J., Riddle, P. J., & Wicker, J. S. (2023). Combatting over-specialization bias in growing chemical databases. Journal of Cheminformatics, 15(1), 53 (17 pp.). https://doi.org/10.1186/s13321-023-00716-w
Denoising single MR spectra by deep learning: miracle or mirage?
Dziadosz, M., Rizzo, R., Kyathanahally, S. P., & Kreis, R. (2023). Denoising single MR spectra by deep learning: miracle or mirage? Magnetic Resonance in Medicine, 90(5), 1749-1761. https://doi.org/10.1002/mrm.29762
Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network
Frank, C., Rußwurm, M., Fluixa-Sanmartin, J., & Tuia, D. (2023). Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network. Frontiers in Water, 5, 1126310 (16 pp.). https://doi.org/10.3389/frwa.2023.1126310
A combination of machine-learning and eDNA reveals the genetic signature of environmental change at the landscape levels
Keck, F., Brantschen, J., & Altermatt, F. (2023). A combination of machine-learning and eDNA reveals the genetic signature of environmental change at the landscape levels. Molecular Ecology, 32(17), 4791-4800. https://doi.org/10.1111/mec.17073
Intelligent multispectral vision system for non-contact water quality monitoring for wastewater
Preitner, K., Blanc, S., Honzatko, D., Kündig, C., Pad, P., Saeedi, S., … Dunbar, L. A. (2023). Intelligent multispectral vision system for non-contact water quality monitoring for wastewater. In B. Jalali & Kichi Kitayama (Eds.), Proceedings of SPIE: Vol. 12438. AI and optical data sciences IV (p. 124380V (13 pp.). https://doi.org/10.1117/12.2649921
Predicting microbial water quality in on-site water reuse systems with online sensors
Reynaert, E., Steiner, P., Yu, Q., D'Olif, L., Joller, N., Schneider, M. Y., & Morgenroth, E. (2023). Predicting microbial water quality in on-site water reuse systems with online sensors. Water Research, 240, 120075 (13 pp.). https://doi.org/10.1016/j.watres.2023.120075
Combining environmental DNA with remote sensing variables to map fish species distributions along a large river
Zong, S., Brantschen, J., Zhang, X., Albouy, C., Valentini, A., Zhang, H., … Pellissier, L. (2023). Combining environmental DNA with remote sensing variables to map fish species distributions along a large river. Remote Sensing in Ecology and Conservation. https://doi.org/10.1002/rse2.366
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
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