Research Project Proposals

Risk, Climate Change and Sustainable Development

Title: Flood risk assessment in a changing environment

Proposer(s): Roberto Rudari, Eva Trasforini, Tatiana Ghizzoni

Curriculum: Risk, Climate Change and Sustainable Development

Description:

This research aims at initiating a groundbreaking approach for risk assessment.

Standard approaches adopted in risk assessment estimate risk as a snapshot of specific hazard, exposure and vulnerability conditions. However, there is a clear feedback between the sequence of disasters materializing, the mitigation measures adopted to contrast risk and how the riskscape develops. The evolution of risk is dominated by a complex interconnection between risk perception, influenced by shocks, and reactions to these shocks, so that strong nonlinearities might determine very different risk conditions in future according to the time sequence of disasters and the way society decides to respond to them.

Independence among hazard, exposure and vulnerability is therefore a common oversimplification in current risk assessment practice. Exploring the correlations among the risk factors may be a key ingredient for a novel dynamical approach to modeling risk evolution over time. This research should move in the direction of risk as a result of a continuous change in hazard, exposure and vulnerability conditions. The new approach should be able to assess the impact of flood events over time, taking into consideration the effects that a specific event can have on the society and the environment in terms, for example, of adaptation or risk reduction measures. Changes in the built-up environment as well as in vulnerability due to antecedent events should be also included in the analysis.

Link to the group or personal webpage

  • http://www.cimafoundation.org/


References:

  1. C. Arrighi, L. Rossi, E. Trasforini, R. Rudari, L. Ferraris, M. Brugioni, S. Franceschini, F. Castelli: Quantification of flood risk mitigation benefits: A building-scale damage assessment through the RASOR platform, Journal of Environmental Management 207 (2018) 92 - 104

  2. J. Becker, L. Rossi, S. De Angeli, R. Rudari, E. Trasforini: Impact assessment on global scale with the RASOR platform. Living Planet Symposium 2016, Prague.

  3. ​CIMA, UNDRR: Angola Disaster Risk Profile, Nairobi: UNDRR and CIMA Research Foundation, 2019

  4. CIMA, UNDRR: Tanzania Disaster Risk Profile, Nairobi: UNDRR and CIMA Research Foundation, 2019

  5. CIMA, UNDRR: Zambia Disaster Risk Profile, Nairobi: UNDRR and CIMA Research Foundation, 2019

  6. G. Di Baldassarre, A. Viglione, G. Carr, L. Kuil, J.L. Salinas, G. Blöschl: Socio-hydrology: conceptualising human-flood interactions, Hydrology and Earth System Sciences 17 (8), 3295-3303, 2013

  7. ​G. Di Baldassarre, A. Viglione, G. Carr, L. Kuil, K. Yan, L. Brandimarte: Debates—Perspectives on socio-hydrology: Capturing feedbacks between physical and social processes, Water Resources Research 51 (6), 4770-4781, 2015.

  8. P. Gober and H.S. Wheater: Perspective on socio-hydrology: Modeling flood risk as a public policy problem, Water Resour. Res., 51, doi:10.1002/2015WR016945, 2015

  9. F.N. Koudogbo, R. Rudari, A. Eddy, E. Trasforini, L. Rossi, H. Yésou, J. Beckers, F. Dell'acqua, M. Huber, A. Roth, S. Salvi, A. Ganas: EO data for rapid risk analysis with the RASOR platform, Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International.

  10. F. Silvestro, N. Rebora, L. Rossi, D. Dolia, S. Gabellani, F. Pignone, E. Trasforini, R. Rudari, S. De Angeli, and C. Masciulli What if the 25 October 2011 event that struck Cinque Terre (Liguria) had happened in Genoa, Italy? Flooding scenarios, hazard mapping and damage estimation, Nat. Hazards Earth Syst. Sci., 16, 1737–1753, 2016

  11. A. Viglione, G. Di Baldassarre, L. Brandimarte, L. Kuil, G. Carr, J.L. Salinas: Insights from socio-hydrology modelling on dealing with flood risk–roles of collective memory, risk-taking attitude and trust, Journal of Hydrology 518, 71-82, 201

Title: Weather extremes forecasting and AI

Proposer(s): Antonio Parodi & Luca Oneto

Curriculum: Risk, Climate Change and Sustainable Development

Description:

Artificial intelligence (AI), machine learning, deep learning, as well as data volumes are developing at an unprecedented rate in many different scientific sectors. Weather and climate modelling represent research and operational domains that can certainly greatly benefit from aforementioned developments, thus evolving towards an even more pronounced data centric vision.

On the one side numerical weather predictions models are being executed at higher and higher spatio-temporal resolution to target nowcasting and very short-range predictions horizons for rapidly evolving and often hardly predictable fine-scale weather phenomena, under growingly impact of ongoing climate change phenomena. On the other side a very rich portfolio of weather monitoring techniques including remote sensing (weather radar, Copernicus products, etc), standard in situ weather stations (authoritative and personal ones), monitoring webcams, and other low-cost sensors offer an increasingly amount of observational data to be fully integrated in a quality controlled and quality assured manner in the current prediction workflows.

High-Performance Big Data Analytics (HPDA) represents the natural realm where weather modelling and AI can find an effective synthesis via a tight integration of computing systems combining HPC, Cloud, and IoT solutions with machine learning and deep learning techniques.

The proposed research activities will target the improvement of the full prediction workflow in support of end to end early warning systems, including observational data quality control and quality assurance, bias correction learned from data assimilation, as well as improving forecast skills for highly localized phenomena (rainfall, lightning, hailstorms, etc) via hybridization or post-processing with machine learning and deep learning techniques. The project will benefit of the CIMA Foundation datasets both from high-resolution weather and climate scenarios, as well as from observational data (radar national mosaic, in situ weather stations, satellite data etc). CIMA will also support the modelling activities with in-house computing resources (a recently acquired 1600 cores server funded by Finanziaria Ligure per lo Sviluppo Economico (FILSE)-Liguria Region) and external ones (e.g. CINECA). The work will be undertaken also in cooperation with Antonella Galizia (IMATI-CNR), Massimo Milelli (CIMA), and Andrea Parodi (CIMA).

Link to the group or personal webpage

References:

  1. Cipollini, F., Miglianti, F., Oneto, L., Tani, G., Viviani, M., Anguita, D. (2019), “Cavitation Noise Spectra Prediction with Hybrid Models.” In INNS Big Data and Deep Learning conference (pp. 152-157).

  2. Düben, P. et al., 2021, “Machine learning at ECMWF: A roadmap for the next 10 years”, ECMWF Technical Memoranda 878, 01/2021, http://dx.doi.org/10.21957/ge7ckgm

  3. Garbero, V., Milelli, M., Bucchignani, E., Mercogliano, P., Varentsov, M., Rozinkina, I., ... & Repola, F. (2021). Evaluating the Urban Canopy Scheme TERRA_URB in the COSMO Model for Selected European Cities. Atmosphere, 12(2), 237.

  4. Ghelardoni, L., Ghio, A., Anguita, D. (2013). Energy load forecasting using empirical mode decomposition and support vector regression. IEEE Transactions on Smart Grid, 4(1), 549-556.

  5. Parodi, A., Kranzlmüller, D., Clematis, A., Danovaro, E., Galizia, A., Garrote, L., ... & D’Agostino, D. (2017). DRIHM (2US): an e-science environment for hydrometeorological research on high-impact weather events. Bulletin of the American Meteorological Society, 98(10), 2149-2166.

  6. Quarati, A., Danovaro, E., Galizia, A., Clematis, A., D’Agostino, D., & Parodi, A. (2015). Scheduling strategies for enabling meteorological simulation on hybrid clouds. Journal of Computational and Applied Mathematics, 273, 438-451.

  7. Reichstein, M., G. Camps-Valls, B. Stevens et al., 2019 “Deep learning and process understanding for data-driven Earth system science”. Nature, 566, 195–204, https://doi.org/10.1038/s41586-019- 09121

  8. Stoica, I. et al., 2017 “A Berkeley viewI of systems challenges for AI”. Technical Report No. UCB/EECS-2017-159, 10/2017 https://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-159.pdf

  9. Vannitsem, S. et al., 2020 “Statistical Postprocessing for Weather Forecasts – Review, Challenges and Avenues in a Big Data World”. Bulletin of the American Meteorological Society, doi: https://doi.org/10.1175/BAMS-D- 19-0308.1