Articles | Volume 22, issue 11
https://doi.org/10.5194/nhess-22-3751-2022
https://doi.org/10.5194/nhess-22-3751-2022
Research article
 | 
22 Nov 2022
Research article |  | 22 Nov 2022

Landsifier v1.0: a Python library to estimate likely triggers of mapped landslides

Kamal Rana, Nishant Malik, and Ugur Ozturk

Related authors

Preface: Estimating and predicting natural hazards and vulnerabilities in the Himalayan region
Wolfgang Schwanghart, Ankit Agarwal, Kristen Cook, Ugur Ozturk, Roopam Shukla, and Sven Fuchs
Nat. Hazards Earth Syst. Sci., 24, 3291–3297, https://doi.org/10.5194/nhess-24-3291-2024,https://doi.org/10.5194/nhess-24-3291-2024, 2024
Short summary
More than heavy rain turning into fast-flowing water – a landscape perspective on the 2021 Eifel floods
Michael Dietze, Rainer Bell, Ugur Ozturk, Kristen L. Cook, Christoff Andermann, Alexander R. Beer, Bodo Damm, Ana Lucia, Felix S. Fauer, Katrin M. Nissen, Tobias Sieg, and Annegret H. Thieken
Nat. Hazards Earth Syst. Sci., 22, 1845–1856, https://doi.org/10.5194/nhess-22-1845-2022,https://doi.org/10.5194/nhess-22-1845-2022, 2022
Short summary
Spatiotemporal patterns of synchronous heavy rainfall events in East Asia during the Baiu season
Frederik Wolf, Ugur Ozturk, Kevin Cheung, and Reik V. Donner
Earth Syst. Dynam., 12, 295–312, https://doi.org/10.5194/esd-12-295-2021,https://doi.org/10.5194/esd-12-295-2021, 2021
Short summary
Optimal design of hydrometric station networks based on complex network analysis
Ankit Agarwal, Norbert Marwan, Rathinasamy Maheswaran, Ugur Ozturk, Jürgen Kurths, and Bruno Merz
Hydrol. Earth Syst. Sci., 24, 2235–2251, https://doi.org/10.5194/hess-24-2235-2020,https://doi.org/10.5194/hess-24-2235-2020, 2020
Short summary
Effects of finite source rupture on landslide triggering: the 2016 Mw 7.1 Kumamoto earthquake
Sebastian von Specht, Ugur Ozturk, Georg Veh, Fabrice Cotton, and Oliver Korup
Solid Earth, 10, 463–486, https://doi.org/10.5194/se-10-463-2019,https://doi.org/10.5194/se-10-463-2019, 2019
Short summary

Related subject area

Landslides and Debris Flows Hazards
Landslide activation during deglaciation in a fjord-dominated landscape: observations from southern Alaska (1984–2022)
Jane Walden, Mylène Jacquemart, Bretwood Higman, Romain Hugonnet, Andrea Manconi, and Daniel Farinotti
Nat. Hazards Earth Syst. Sci., 25, 2045–2073, https://doi.org/10.5194/nhess-25-2045-2025,https://doi.org/10.5194/nhess-25-2045-2025, 2025
Short summary
Brief communication: Weak correlation between building damage and loss of life from landslides
Maximillian Van Wyk de Vries, Alexandre Dunant, Amy L. Johnson, Erin L. Harvey, Sihan Li, Katherine Arrell, Jeevan Baniya, Dipak Basnet, Gopi K. Basyal, Nyima Dorjee Bhotia, Simon J. Dadson, Alexander L. Densmore, Tek Bahadur Dong, Mark E. Kincey, Katie Oven, Anuradha Puri, and Nick J. Rosser
Nat. Hazards Earth Syst. Sci., 25, 1937–1942, https://doi.org/10.5194/nhess-25-1937-2025,https://doi.org/10.5194/nhess-25-1937-2025, 2025
Short summary
Comparative analysis of μ(I) and Voellmy-type grain flow rheologies in geophysical mass flows: insights from theoretical and real case studies
Yu Zhuang, Brian W. McArdell, and Perry Bartelt
Nat. Hazards Earth Syst. Sci., 25, 1901–1912, https://doi.org/10.5194/nhess-25-1901-2025,https://doi.org/10.5194/nhess-25-1901-2025, 2025
Short summary
Exploring implications of input parameter uncertainties in glacial lake outburst flood (GLOF) modelling results using the modelling code r.avaflow
Sonam Rinzin, Stuart Dunning, Rachel Joanne Carr, Ashim Sattar, and Martin Mergili
Nat. Hazards Earth Syst. Sci., 25, 1841–1864, https://doi.org/10.5194/nhess-25-1841-2025,https://doi.org/10.5194/nhess-25-1841-2025, 2025
Short summary
Hillslope-Torrential Hazard Cascades in Tropical Mountains
Maria Isabel Arango-Carmona, Paul Voit, Marcel Hürlimann, Edier Aristizábal, and Oliver Korup
EGUsphere, https://doi.org/10.5194/egusphere-2025-1698,https://doi.org/10.5194/egusphere-2025-1698, 2025
Short summary

Cited articles

Adams, H., Emerson, T., Kirby, M., Neville, R., Peterson, C., Shipman, P., Chepushtanova, S., Hanson, E., Motta, F., and Ziegelmeier, L.: Persistence images: A stable vector representation of persistent homology, J. Mach. Learn. Res., 18, 1–35, 2017. a
Albawi, S., Mohammed, T. A., and Al-Zawi, S.: Understanding of a convolutional neural network, in: 2017 IEEE International Conference on Engineering and Technology (ICET), 21–23 August 2017, Antalya, Turkey, 1–6, https://doi.org/10.1109/ICEngTechnol.2017.8308186, 2017. a, b
Amato, G., Palombi, L., and Raimondi, V.: Data-driven classification of landslide types at a national scale by using Artificial Neural Networks, Int. J. Appl. Earth Obs. Geoinf., 104, 102549, https://doi.org/10.1016/j.jag.2021.102549, 2021. a
Aurisano, A., Radovic, A., Rocco, D., Himmel, A., Messier, M., Niner, E., Pawloski, G., Psihas, F., Sousa, A., and Vahle, P.: A convolutional neural network neutrino event classifier, J. Instrument., 11, P09001, https://doi.org/10.1088/1748-0221/11/09/P09001, 2016. a
Behling, R., Roessner, S., Segl, K., Kleinschmit, B., and Kaufmann, H.: Robust automated image co-registration of optical multi-sensor time series data: Database generation for multi-temporal landslide detection, Remote Sens., 3, 2572–2600, 2014. a
Download
Short summary
The landslide hazard models assist in mitigating losses due to landslides. However, these models depend on landslide databases, which often have missing triggering information, rendering these databases unusable for landslide hazard models. In this work, we developed a Python library, Landsifier, consisting of three different methods to identify the triggers of landslides. These methods can classify landslide triggers with high accuracy using only a landslide polygon shapefile as an input.
Share
Altmetrics
Final-revised paper
Preprint