Langezijds

M. Moreno Zapata (Mateo)

PhD Candidate
ITC-AES
Langezijds 1415
m.morenozapata@utwente.nl
+31534897516
Business card

My research focuses on natural hazard modeling, particularly landslides and wildfires.

Expertise

  • Earth and Planetary Sciences

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

Organisations

Publications

2025

A dynamic landslide model for early warnings in Colombia's roads (2025)[Contribution to conference › Abstract] EGU General Assembly 2025. Urueña Ramirez, D. A., Moreno, M., Lombardo, L., Gómez, D., Vega, J. & van Westen, C.https://doi.org/10.5194/egusphere-egu25-20285Space-time data-driven modeling of wildfire initiation in the mountainous region of Trentino-Alto Adige, Italy (2025)[Contribution to conference › Abstract] EGU General Assembly 2025. Moreno, M., Steger, S., Bozzoli, L., Terzi, S., Trucchia, A., van Westen, C. & Lombardo, L.https://doi.org/10.5194/egusphere-egu25-17023Data-driven modeling of mass movement damage potential across the Alpine Space: A step toward impact-based early warning (2025)[Contribution to conference › Abstract] EGU General Assembly 2025. Steger, S., Spiekermann, R., Lehner, S., Enigl, K., Moreno, M., Crespi, A. & Schlögl, M.https://doi.org/10.5194/egusphere-egu25-9819Software. Reproducible results. Modeling the area of co-seismic landslides via data-driven models: The Kaikōura example (2025)[Dataset Types › Dataset]. Zenodo. Moreno, M.https://doi.org/10.5281/zenodo.15028434Comprehensive multi-hazard risk assessment in data-scarce regions: A study focused on Burundi (2025)[Working paper › Preprint]. European Geosciences Union (E-pub ahead of print/First online). Delves, J., Renner, K., Campalani, P., Piñón, J., Schneiderbauer, S., Steger, S., Moreno, M., Oterino, M. B. B., Perez, E. & Pittore, M.https://doi.org/10.5194/egusphere-2024-3445

2024

Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy (2024)[Working paper › Preprint]. Earth ArXiv. Moreno, M., Lombardo, L., Steger, S., de Vugt, L., Zieher, T., Crespi, A., Marra, F., van Westen, C. & Opitz, T.https://doi.org/10.31223/X5VB0MSoftware. Reproducible results. Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy (2024)[Dataset Types › Dataset]. Zenodo. Moreno, M.https://doi.org/10.5281/zenodo.15033257A benchmark dataset and workflow for landslide susceptibility zonation (2024)Earth-science reviews, 258. Article 104927. Alvioli, M., Loche, M., Jacobs, L., Grohmann, C. H., Abraham, M. T., Gupta, K., Satyam, N., Scaringi, G., Bornaetxea, T., Rossi, M., Marchesini, I., Lombardo, L., Moreno, M., Steger, S., Camera, C. A. S., Bajni, G., Samodra, G., Wahyudi, E. E., Susyanto, N., … Rivera-Rivera, J.https://doi.org/10.1016/j.earscirev.2024.104927Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds (2024)Geoscience 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.https://doi.org/10.1016/j.gsf.2024.101822Spatial transferability of the physically based model TRIGRS using parameter ensembles (2024)Earth surface processes and landforms, 49(4), 1330-1347. de Vugt, L., Zieher, T., Schneider‐Muntau, B., Moreno, M., Steger, S. & Rutzinger, M.https://doi.org/10.1002/esp.5770

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 shallOw 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. 

EO4MULTIHA: Earth Observation for Multi-Hazard Risk Assessments
The EO4MULTIHA is a European Space Agency funded project aiming to explore the EO technology potential to advance the scientific understanding of high impact multi-hazard events to better identify, characterise and assess their associated risk, vulnerability and impacts on society and ecosystems. 

Address

University of Twente

Langezijds (building no. 19), room 1415
Hallenweg 8
7522 NH Enschede
Netherlands

Navigate to location

Organisations

Scan the QR code or
Download vCard