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.


  • Earth and Planetary Sciences

    • Landslide
    • Model
    • Datum
    • Italy
    • Prediction
    • Modeling
    • Time
    • Area



Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholdsGeoscience Frontiers, 15(5), Article 101822. Steger, S., Moreno, M., Crespi, A., Luigi Gariano, S., Brunetti, M. T., Melillo, M., Peruccacci, S., Marra, F., de Vugt, L., Zieher, T., Rutzinger, M., Mair, V. & Pittore, M. transferability of the physically based model TRIGRS using parameter ensemblesEarth surface processes and landforms, 49(4), 1330-1347. de Vugt, L., Zieher, T., Schneider‐Muntau, B., Moreno, M., Steger, S. & Rutzinger, M. climate change projections into operational debris flow hazard mapping: Initial insights from the Toverino River Basin in South Tyrol (Eastern Italian Alps).. Bozzoli, L., Crespi, A., Steger, S. & Moreno, M. of beta regression for the prediction of landslide areal density in South Tyrol, Italy . Moreno, M., Opitz, T., Steger, S., Westen, C. v. & Lombardo, L. of a data-driven space-time model to predict precipitation-induced geomorphic impact events at the Alpine Scale. Spiekermann, R., Lehner, S., Steger, S., Moreno, M., Enigl, K., Imgrüth, D., Schlögl, M. & Pistotnik, G. data-driven modeling of precipitation-induced shallow landslides in South Tyrol, ItalyScience of the total environment, 912(169166), Article 169166, 1-17. Moreno, M., Lombardo, L., Crespi, A., Zellner, P. J., Mair, V., Pittore, M., van Westen, C. J. & Steger, S.

Research profiles

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. 

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