|
|
|
Alvioli, M., Marchesini, I., Reichenbach, P., Rossi, M., Ardizzone, F., Fiorucci, F., et al. (2016). Automatic delineation of geomorphological slope units with r. slopeunits v1. 0 and their optimization for landslide susceptibility modeling. Geosci. Model Dev. 9, 3975–3991.
|
|
|
|
Bedford T.J. and Cooke R.M., (2001), Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial Intelligence, 32:245–268.
|
|
|
|
Bedford T.J. and Cooke R.M., (2002), Vines — a new graphical model for dependent random variables. Ann. of Stat., 30(4):1031–1068.
|
|
|
|
Brabb, E. E. (1984). Innovative approaches to landslide hazard and risk mapping., in International Landslide Symposium Proceedings, Toronto, Canada, 17–22.
|
|
|
|
Caleca, F., Confuorto, P., Raspini, F., Segoni, S., Tofani, V., Casagli, N., et al. (2024). Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning. Sci. Total Environ. 950, 175277.
|
|
|
|
Caleca, F., Lombardo, L., Steger, S., Tanyas, H., Raspini, F., Dahal, A., et al. (2025). Pan‐European landslide risk assessment: From theory to practice. Rev. Geophys. 63, e2023RG000825.
|
|
|
|
Corominas, J., Guzzetti, F., Lan, H., Macciotta, R., Marunteranu, C., McDougall, S., et al. (2023). Revisiting landslide risk terms: IAEG commission C-37 working group on landslide risk nomenclature. Bull. Eng. Geol. Environ. 82, 450.
|
|
|
|
Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.-P., Fotopoulou, S., et al. (2014). Recommendations for the quantitative analysis of landslide risk. Bull. Eng. Geol. Environ. 73, 209–263.
|
|
|
|
Cruden, D. M., and Varnes, D. J. (1996). Landslide types and processes In: Turner, KA; Schuster, RL, eds. Landslides-investigation and mitigation. National Research Council Transportation Research Board Special Report 247. Washington, DC: National Academy Press.
|
|
|
|
Gomes, P. I., Aththanayake, U., Deng, W., Li, A., Zhao, W., and Jayathilaka, T. (2020). Ecological fragmentation two years after a major landslide: Correlations between vegetation indices and geo-environmental factors. Ecol. Eng. 153, 105914.
|
|
|
|
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli, M., and Ardizzone, F. (2005). Probabilistic landslide hazard assessment at the basin scale. Geomorphology 72, 272–299. doi: https://doi.org/10.1016/j.geomorph.2005.06.002
|
|
|
|
Hastie, T., and Tibshirani, R. (1987). Generalized additive models: some applications. J. Am. Stat. Assoc. 82, 371–386.
|
|
|
|
Hungr, O., Leroueil, S., and Picarelli, L. (2014). The Varnes classification of landslide types, an update. Landslides 11, 167–194.
|
|
|
|
Joe, H. (2014). Dependence modeling with copulas. Boca Raton, FL: Chapman & Hall/CRC.
|
|
|
|
Maxwell, A. E., Sharma, M., and Donaldson, K. A. (2021). Explainable boosting machines for slope failure spatial predictive modeling. Remote Sens. 13, 4991.
|
|
|
|
Merghadi, A., Yunus, A. P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D. T., et al. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Sci. Rev. 207, 103225.
|
|
|
|
Mondini, A. C., Chang, K.-T., and Yin, H.-Y. (2011). Combining multiple change detection indices for mapping landslides triggered by typhoons. Geomorphology 134, 440–451.
|
|
|
|
Nagler, T., & Vatter, T. (2025), Pyvinecopulib, Zenodo. https://doi.org/10.5281/zenodo.14841456
|
|
|
|
Nori, H., Jenkins, S., Koch, P., and Caruana, R. (2019). Interpretml: A unified framework for machine learning interpretability. ArXiv Prepr. ArXiv190909223.
|
|
|
|
Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., and Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Sci. Rev. 180, 60–91.
|
|
|
|
Tarquini, S., Isola, I., Favalli, M., Battistini, A., and Dotta, G. T. (2023). a Digital Elevation Model of Italy with a 10 Meters Cell Size (Version 1.1). Ist. Naz. Geofis. E Vulcanol. INGV Roma Italy 1, 1–2.
|
|
|
|
Tavakkoli Piralilou, S., Shahabi, H., Jarihani, B., Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., et al. (2019). Landslide detection using multi-scale image segmentation and different machine learning models in the higher himalayas. Remote Sens. 11, 2575.
|
|
|
|
USDA-SCS (1986). Urban hydrology for small watersheds. United States Department of Agriculture, Soil Conservation Service, Engineering Division. |
|
|
|
- Arrighi, C., Pregnolato, M., Castelli, F.,2021. Indirect flood impacts and cascade risk across interdependent linear infrastructures. *Nat. Haz. Earth Sys. Sci.*, **21** (6), 1955-1969.
|
|
|
|
- Arrighi, C., Rossi, L., Trasforini, E., Rudari, R., Ferraris, L., Brugioni, M., Franceschini, S., and F. Castelli, 2018. Quantification of flood risk mitigation benefits: A building-scale damage assessment through the RASOR platform. *J. Env. Manag.*, **207**, 92-104.
|
|
|
|
- Cheng, L., AghaKouchak, A., Gilleland, E., Katz, R. W., 2014. Non-stationary extreme value analysis in a changing climate. *Climatic Change*, **127**, 353–369.
|
|
|
|
- Das, J., Umamahesh, N.V., 2022. Investigating risk, reliability and return period under the influence of large scale modes, and regional hydrological variability in hydrologic extreme. *Hydrol. Sci. J.*, **67**:1, 65-81.
|
|
|
|
- Iliopoulou, T., Koutsoyiannis, D., 2020. Projecting the future of rainfall extremes: Better classic than trendy. *J. Hydrol.*, **588**, 125005.
|
|
|
|
- MASE, 2023. *Piano Nazionale di Adattamento ai Cambiamenti Climatici*. https://www.mase.gov.it/notizie/clima-approvato-il-pianonazionale-
|
|
|
|
di-adattamento-ai-cambiamenti-climatici.
|
|
|
|
- Masi, M., Arrighi, C., Piragino, F., Castelli, F., 2024. Participatory multi-criteria decision making for optimal siting of multipurpose artificial reservoirs. *J. Flood Risk Manag.*, **370**, 122904.
|
|
|
|
- Molinari, D., Rita Scorzini, A., Arrighi, C., Carisi, F., Castelli, F., Domeneghetti, A., Gallazzi, A., Galliani, M., Grelot, F., Kellermann, P., Kreibich, H., Mohor, G.S., Mosimann, M., Natho, S., Richert, C., Schroeter, K., Thieken, A.H., Paul Zischg, A., Ballio, F., 2020. Are flood damage models converging to "reality"? Lessons learnt from a blind test. *Nat. Haz. Earth Sys. Sci.*, **20** (11), 2997-3017.
|
|
|
|
- Serinaldi, F., Grimaldi, S., 2007. Fully nested 3-copula: Procedure and application on hydrological data. *J. Hydraul. Eng.*, **12**, 420 – 430.
|
|
|
|
- Silva, D.F, Simonovic, S.P, Schardong, A., Goldenfum, J.A., 2021. Assessment of non-stationary IDF curves under a changing climate: Case study of different climatic zones in Canada. *J. Hydrol.*, **36**, 100870.
|
|
|
|
- Vergouwe, R., 2016. *The National Flood Risk Analysis for the Netherlands, FINAL REPORT*. Rijkswaterstaat VNK Project Office.
|
|
|
|
- Vinnarasi, R., Dhanya, C.T., 2022. Time-varying Intensity-Duration-Frequency relationship through climate-informed covariates. *J. Hydrol.*, **604**, 127178.
|
|
|
|
- Vogel, R. M., Castellarin, A., 2017. Risk, reliability, and return periods and hydrologic design. In: *Handbook of applied hydrology (2nd ed.)*, pp. 78.1-78.10, New York, USA. |
|
|
\ No newline at end of file |
