About Me
My research focuses on the field of natural hazards, particularly on landslide modeling. I'm currently working on the project "PROSLIDE - PRediction of ShallOw LandSLIDEs" in the province of South Tyrol, Italy.
Organisations
Publications
Recent
Moreno, M., Steger, S.
, Tanyas, H.
, & Lombardo, L. (2023).
Modeling the area of co-seismic landslides via data-driven models: The Kaikōura example.
Engineering geology,
320, [107121].
https://doi.org/10.1016/j.enggeo.2023.107121
Steger, S.
, Moreno, M., Crespi, A., Zellner, P. J., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., Marra, F., Kohrs, R., Goetz, J., Mair, V., & Pittore, M. (2023).
Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models.
Natural hazards and earth system sciences,
23(4), 1483-1506.
https://doi.org/10.5194/nhess-23-1483-2023
Steger, S.
, Moreno, M., Crespi, A., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., Marra, F., Borga, M., Vugt, L. D., Zieher, T., Rutzinger, M., Mair, V., Campalani, P., & Pittore, M. (2023).
A data-driven approach to derive spatially explicit dynamic "thresholds" for shallow landslide occurrence in South Tyrol (Italy). Abstract from EGU General Assembly 2023, Vienna, Austria.
https://doi.org/10.5194/egusphere-egu23-1353
Vugt, L. D., Zieher, T., Schneider-Muntau, B.
, Moreno, M., Steger, S., & Rutzinger, M. (2023).
Improving the performance of a dynamic slope stability model (TRIGRS) with integrated spatio-temporal precipitation data. Abstract from EGU General Assembly 2023, Vienna, Austria.
https://doi.org/10.5194/egusphere-egu23-7845
Moreno, M., & Steger, S. (2023).
Slope unit size matters - why should the areal extent of slope units be considered in data-driven landslide susceptibility models?. Abstract from EGU General Assembly 2023, Vienna, Austria.
https://doi.org/10.5194/egusphere-egu23-12943
Moreno, M., Steger, S.
, Lombardo, L., Opitz, T., Crespi, A., Marra, F., Vugt, L. D., Zieher, T., Rutzinger, M., Mair, V., Pittore, M.
, & van Westen, C. (2023).
Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy . Abstract from EGU General Assembly 2023, Vienna, Austria.
https://doi.org/10.5194/egusphere-egu23-9538
Steger, S.
, Moreno, M., Crespi, A., Zellner, P. J., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., Marra, F., Kohrs, R., Goetz, J., Mair, V., & Pittore, M. (2022, Nov 17).
Supplementary material to "Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models".
https://doi.org/10.5194/nhess-2022-271-supplement
Pittore, M., Steger, S.
, Moreno, M., Campalani, P., Renner, K., Villacis, C., Piñón, J., Pérez, E., Rosa, L. R. D. L., Achour, I., & Noel, E. (2022).
Towards a quantitative spatiotemporal assessment of probabilistic landslide risk for large-area applications: challenges and perspectives. Abstract from EGU General Assembly 2022, Vienna, Austria.
https://doi.org/10.5194/egusphere-egu22-11082
Steger, S., Kohrs, R., Crespi, A.
, Moreno, M., Zellner, P. J., Goetz, J., Mair, V., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., & Pittore, M. (2022).
A data-driven approach to establish prediction surfaces for rainfall-induced shallow landslides in South Tyrol, Italy. Abstract from EGU General Assembly 2022, Vienna, Austria.
https://doi.org/10.5194/egusphere-egu22-5902
Steger, S.
, Moreno, M., Crespi, A., Zellner, P. J., Kohrs, R., Goetz, J., Gariano, S. L., Brunetti, M. T., Melillo, M., Peruccacci, S., Vugt, L. D., Zieher, T., Rutzinger, M., Mair, V., & Pittore, M. (2022).
Applying a hierarchical Generalized Additive Model to integrate predisposing, preparatory and triggering factors for landslide prediction. Abstract from 10th International Conference on Geomorphology, Coimbra, Portugal.
https://doi.org/10.5194/icg2022-388
UT Research Information System
Google Scholar Link
Affiliated Study Programmes
Master
Projects
PROSLIDE: Integration of static and dynamic landslide controls at multiple-scales using data-driven and physically-based methods – exploring new opportunities for the
PRediction of shall
Ow land
SLIDEs
The overarching aim of PROSLIDE is to exploit the potential of innovative input data, available ground truth data and novel modelling designs (i.e. data-driven and physically-based) at different scales to improve the predictability of where and when landslides will occur.
The overarching aim of PROSLIDE is to exploit the potential of innovative input data, available ground truth data and novel modelling designs (i.e. data-driven and physically-based) at different scales to improve the predictability of where and when landslides will occur.
Tweets
Contact Details
Visiting Address
University of Twente
Drienerlolaan 5
7522 NB Enschede
The Netherlands
Mailing Address
University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands