M. Moreno Zapata (Mateo)

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.

Faculty of Geo-Information Science and Earth Observation (ITC), Department of Applied Earth Sciences (AES)

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

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Master

Geo-Information Science and Earth Observation

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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+31534897516

+390471055387

m.morenozapata@utwente.nl

mateo.morenozapata@eurac.edu