64 citations as recorded by crossref.

1. Agent‐based Models and the Spatial Sciences P. Torrens https://doi.org/10.1111/j.1749-8198.2009.00311.x
2. A two scale cellular automaton for flow dynamics modeling (2CAFDYM) H. Kassogué et al. https://doi.org/10.1016/j.apm.2016.10.034
3. Modeling debris flow initiation and run-out in recently burned areas using data-driven methods R. Melo & J. Zêzere https://doi.org/10.1007/s11069-017-2921-4
4. Efficient Urban Inundation Model for Live Flood Forecasting with Cellular Automata and Motion Cost Fields M. Issermann et al. https://doi.org/10.3390/w12071997
5. Hydrodynamic and topography based cellular automaton model for simulating debris flow run-out extent and entrainment behavior Z. Han et al. https://doi.org/10.1016/j.watres.2021.116872
6. REVERSIBILITY ALGORITHMS FOR 3-STATE HEXAGONAL CELLULAR AUTOMATA WITH PERIODIC BOUNDARIES S. UGUZ et al. https://doi.org/10.1142/S0218127413501010
7. Parallel genetic algorithms for optimising cellular automata models of natural complex phenomena: An application to debris flows D. D’Ambrosio et al. https://doi.org/10.1016/j.cageo.2005.10.027
8. On the consistency of urban cellular automata models based on hexagonal and square cells A. Nugraha et al. https://doi.org/10.1177/2399808319898501
9. Prediction of rainfall-induced shallow landslide runout integrating hydro-mechanical slope stability analysis M. Amarasinghe et al. https://doi.org/10.1080/17499518.2025.2607420
10. Enhancing debris flow run-out hazard assessment through probabilistic post-event back analysis P. Zeng et al. https://doi.org/10.1007/s10346-026-02746-5
11. Infiltration effects on a two-dimensional molecular dynamics model of landslides G. Martelloni & F. Bagnoli https://doi.org/10.1007/s11069-013-0944-z
12. Size-frequency distribution of submarine mass movements on the Palomares continental slope (W Mediterranean) L. Retegui et al. https://doi.org/10.1016/j.margeo.2024.107411
13. Runout modelling and hazard assessment of Tangni debris flow in Garhwal Himalayas, India R. Dash et al. https://doi.org/10.1007/s12665-021-09637-z
14. Development of 1D–2D Urban Flood Simulation Model Based on Modified Cellular Automata Approach H. Tavakolifar et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002036
15. The latest release of the lava flows simulation model SCIARA: First application to Mt Etna (Italy) and solution of the anisotropic flow direction problem on an ideal surface W. Spataro et al. https://doi.org/10.1016/j.procs.2010.04.004
16. Integrating geomorphology, statistic and numerical simulations for landslide invasion hazard scenarios mapping: An example in the Sorrento Peninsula (Italy) F. Lucà et al. https://doi.org/10.1016/j.cageo.2014.01.006
17. Modelling of mobility of Rissa landslide and following tsunami Z. Liu et al. https://doi.org/10.1016/j.compgeo.2021.104388
18. A macroscopic collisional model for debris-flows simulation D. D'Ambrosio et al. https://doi.org/10.1016/j.envsoft.2006.09.009
19. An ensemble neural network approach for space–time landslide predictive modelling J. Lim et al. https://doi.org/10.1016/j.jag.2024.104037
20. A progressive entrainment runout model for debris flow analysis and its application C. Kang & D. Chan https://doi.org/10.1016/j.geomorph.2018.09.003
21. 2-Dimensional Reversible Hexagonal Cellular Automata with Periodic Boundary S. Uguz et al. https://doi.org/10.12693/APhysPolA.123.480
22. OpenCAL system extension and application to the three-dimensional Richards equation for unsaturated flow A. De Rango et al. https://doi.org/10.1016/j.camwa.2020.05.017
23. Debris flow-induced topographic changes: effects of recurrent debris flow initiation C. Chen & Q. Wang https://doi.org/10.1007/s10661-017-6169-y
24. A first multi-GPU/multi-node implementation of the open computing abstraction layer A. De Rango et al. https://doi.org/10.1016/j.jocs.2018.09.012
25. Linking rainfall-induced landslides with debris flows runout patterns towards catchment scale hazard assessment L. Fan et al. https://doi.org/10.1016/j.geomorph.2016.10.007
26. Cellular-Automata Model for Dense-Snow Avalanches F. Barpi et al. https://doi.org/10.1061/(ASCE)0887-381X(2007)21:4(121)
27. Towards efficient GPGPU Cellular Automata model implementation using persistent active cells P. Renc et al. https://doi.org/10.1016/j.jocs.2021.101538
28. Cellular automata model of density currents T. Salles et al. https://doi.org/10.1016/j.geomorph.2006.10.016
29. Parallel evolutionary modelling of geological processes D. D’Ambrosio & W. Spataro https://doi.org/10.1016/j.parco.2006.12.003
30. An examination of controls on debris flow mobility: Evidence from coastal British Columbia R. Guthrie et al. https://doi.org/10.1016/j.geomorph.2009.09.021
31. Optimizing the Numerical Simulation of Debris Flows: A New Exploration of the Hexagonal Cellular Automaton Method Z. Han et al. https://doi.org/10.3390/w16111536
32. Phenomenological study of irregular cellular automata based on Lyapunov exponents and Jacobians J. Baetens & B. De Baets https://doi.org/10.1063/1.3460362
33. Numerical Simulation of Debris Flow and Driftwood with Entrainment of Sediment T. Kang et al. https://doi.org/10.3390/w14223673
34. Mass movement hazard & global climate change: Physical-deterministic modelling study of the rest and be thankful pass, Scotland UK N. Klaver et al. https://doi.org/10.1016/j.geomorph.2023.108979
35. Cellular automata on irregular tessellations J. Baetens & B. De Baets https://doi.org/10.1080/14689367.2012.711300
36. SCIARA: cellular automata lava flow modelling and applications in hazard prediction and mitigation R. Rongo et al. https://doi.org/10.1144/SP426.22
37. The importance of entrainment and bulking on debris flow runout modeling: examples from the Swiss Alps F. Frank et al. https://doi.org/10.5194/nhess-15-2569-2015
38. Developments of a flood inundation model based on the cellular automata approach: Testing different methods to improve model performance F. Dottori & E. Todini https://doi.org/10.1016/j.pce.2011.02.004
39. Simulations of landslide hazard scenarios by a geophysical safety factor E. Piegari & R. Di Maio https://doi.org/10.1007/s11069-013-0769-9
40. Structure and reversibility of 2D hexagonal cellular automata I. Siap et al. https://doi.org/10.1016/j.camwa.2011.09.066
41. Parameterization of a numerical 2-D debris flow model with entrainment: a case study of the Faucon catchment, Southern French Alps H. Hussin et al. https://doi.org/10.5194/nhess-12-3075-2012
42. Debris-flow modeling at Meretschibach and Bondasca catchments, Switzerland: sensitivity testing of field-data-based entrainment model F. Frank et al. https://doi.org/10.5194/nhess-17-801-2017
43. Effect of asynchronous updating on the stability of cellular automata J. Baetens et al. https://doi.org/10.1016/j.chaos.2012.01.002
44. Simulating the 1999 Capbreton canyon turbidity current with a Cellular Automata model T. Salles et al. https://doi.org/10.1016/j.geomorph.2007.09.005
45. The Open Computing Abstraction Layer for Parallel Complex Systems Modeling on Many-Core Systems D. D’Ambrosio et al. https://doi.org/10.1016/j.jpdc.2018.07.005
46. SoDA project: A simulation of soil surface degradation by rainfall G. Valette et al. https://doi.org/10.1016/j.cag.2006.03.016
47. A Novel Hybrid Approach Based on Cellular Automata and a Digital Elevation Model for Rapid Flood Assessment O. Wijaya & T. Yang https://doi.org/10.3390/w13091311
48. Numerical modelling of an alpine debris flow by considering bed entrainment Z. Qiao et al. https://doi.org/10.3389/feart.2022.1059525
49. A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change M. Roodposhti et al. https://doi.org/10.1016/j.envsoft.2018.10.006
50. Debris flow run-out simulation and analysis using a dynamic model R. Melo et al. https://doi.org/10.5194/nhess-18-555-2018
51. A 3D model for rain-induced landslides based on molecular dynamics with fractal and fractional water diffusion G. Martelloni et al. https://doi.org/10.1016/j.cnsns.2017.03.014
52. Estimation of the area of sediment deposition by debris flow using a physical-based modeling approach H. An et al. https://doi.org/10.1016/j.quaint.2018.09.049
53. Robustness evaluation of fuzzy expert system and extreme learning machine for geographic information system-based landslide susceptibility zonation: A case study from Indian Himalaya B. Peethambaran et al. https://doi.org/10.1007/s12665-019-8225-0
54. Cellular Automata and Finite Volume solvers converge for 2D shallow flow modelling for hydrological modelling D. Caviedes-Voullième et al. https://doi.org/10.1016/j.jhydrol.2018.06.021
55. Compacity influence in reorganization of two dimensional granular packings: Numerical model M. Aguirre & A. Calvo https://doi.org/10.1016/j.powtec.2010.03.001
56. Applying Cellular Automata and DEVS Methodologies to Digital Games: A Survey G. Wainer et al. https://doi.org/10.1177/1046878110378708
57. Structure and Reversibility of 2D von Neumann Cellular Automata Over Triangular Lattice S. Uguz et al. https://doi.org/10.1142/S0218127417500833
58. 2D Triangular von Neumann Cellular Automata with Periodic Boundary S. Uguz et al. https://doi.org/10.1142/S0218127419500299
59. Two-dimensional numerical model for debris flows in the Jiangjia Gully, Yunnan Province H. Peng et al. https://doi.org/10.1007/s11629-011-2043-5
60. 2014 Canadian Geotechnical Colloquium: Landslide runout analysis — current practice and challenges S. McDougall https://doi.org/10.1139/cgj-2016-0104
61. Combining data-driven models to assess susceptibility of shallow slides failure and run-out R. Melo et al. https://doi.org/10.1007/s10346-019-01235-2
62. Modelling global urban land-use change process using spherical cellular automata B. Addae & S. Dragićević https://doi.org/10.1007/s10708-022-10776-4
63. Modeling shallow landslides and root reinforcement: A review I. Murgia et al. https://doi.org/10.1016/j.ecoleng.2022.106671
64. Landslide Scaling: A Review S. Tebbens https://doi.org/10.1029/2019EA000662