Department Systems Analysis, Integrated Assessment and Modelling

Systems Analysis, Integrated Assessment and Modelling

In SIAM, we develop and apply models and formal techniques in order to understand, demonstrate, and predict the behavior of natural, technical, social and economical systems that pertain to water and other natural resources. Read more

New Publications

Chaparro Pedraza, P. C., Matthews, B., de Meester, L., & Dakos, V. (2021). Adaptive evolution can both prevent ecosystem collapse and delay ecosystem recovery. American Naturalist, 198(6) (13 pp.). doi:10.1086/716929, Institutional Repository
Qi, W., Feng, L., Yang, H., & Liu, J. (2021). Spring and summer potential flood risk in Northeast China. Journal of Hydrology: Regional Studies, 38, 100951 (11 pp.). doi:10.1016/j.ejrh.2021.100951, Institutional Repository
Ashraf Vaghefi, S., Muccione, V., van Ginkel, K. C. H., & Haasnoot, M. (2021). Using decision making under deep uncertainty (DMDU) approaches to support climate change adaptation of Swiss Ski Resorts. Environmental Science and Policy, 126, 65-78. doi:10.1016/j.envsci.2021.09.005, Institutional Repository
Adelisardou, F., Zhao, W., Chow, R., Mederly, P., Minkina, T., & Schou, J. S. (2021). Spatiotemporal change detection of carbon storage and sequestration in an arid ecosystem by integrating Google Earth Engine and InVEST (the Jiroft plain, Iran). International Journal of Environmental Science and Technology. doi:10.1007/s13762-021-03676-6, Institutional Repository
Qi, W., Feng, L., Yang, H., Zhu, X., Liu, Y., & Liu, J. (2021). Weakening flood, intensifying hydrological drought severity and decreasing drought probability in Northeast China. Journal of Hydrology: Regional Studies, 38, 100941 (12 pp.). doi:10.1016/j.ejrh.2021.100941, Institutional Repository

News

[Translate to English:] Forschende der Eawag nutzen Deep-learning-Methoden mit künstlichen neuronalen Netzen, um Plankton automatisch zu klassifizieren. Hintergrund: Aquascope-Aufnahme des Wasserflohs Bosmina. (Foto und Grafik: Eawag)
November 25, 2021 –

Eawag intends to further develop artificial intelligence methods to enable their increasing use in water research. One current application is the monitoring of plankton communities in lakes. With the help of machine learning...

Eawag intends to further develop artificial intelligence methods to enable their increasing use in water research. One current application is the monitoring of plankton communities in lakes. With the help of machine learning methods, it has been possible to implement an automatic classification of the microorganisms.   

Read more

Events

15.09.​2022,
9.00 am
Swiss Tech Convention Center Lausanne

Projects

Bridging the gap between data science and mechanistic modelling to gain knowledge about community assembly
Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.
Deep Neural Networks (DNNs) have shown empirical performance but they are still nevertheless a black-box function modeling data
Scalable Bayesian inference framework for uncertainty quantification in stochastic models using thousands of processors in parallel at the Swiss Supercomputing Center and ETH Zurich.
Mechanistic modelling of the macroinvertebrate community composition in rivers.
We compare invasions in aquatic and terrestrial ecosystems primarily at large (national) spatial scales and among several higher-level taxa (insects, molluscs, crustaceans, all major vertebrate classes, and plants).
We use machine learning methods to predict the effects of chemicals on aquatic species.
Development of a semi-distributed hydrological model with a “flexible” approach. Testing and comparing of different model structures to combine modeling and experimenting into a learning process.