26 citations as recorded by crossref.

1. A One-Way Coupled Navier–Stokes-Serre Model for Simulating the Generation and Propagation of Tsunami Waves Z. Xin et al. https://doi.org/10.1007/s00024-024-03485-1
2. Development and validation of a two-layer Boussinesq model for simulating free surface waves generated by bottom motion K. Fang et al. https://doi.org/10.1016/j.apor.2019.101977
3. On water waves generated by a bottom obstacle translating at a subcritical speed P. Lo & P. Liu https://doi.org/10.1017/jfm.2021.537
4. Overtaking solitary wave collisions for Whitham–Boussinesq systems M. Flamarion & R. Vargas-Magaña https://doi.org/10.1016/j.padiff.2025.101080
5. Bulgarian tsunami on 7 May 2007: numerical investigation of the hypothesis of a submarine-landslide origin O. Gusev et al. https://doi.org/10.1144/SP477.6
6. Analytical three-periodic solution and interaction for nonlocal Boussinesq equation M. Chen & Z. Wang https://doi.org/10.1140/epjp/s13360-023-04518-9
7. Smoothed Particle Hydrodynamics for modelling landslide–water interaction problems A. Mahallem et al. https://doi.org/10.1007/s10346-021-01807-1
8. A rigorous finite volume model to simulate subaerial and submarine landslide-generated waves S. Yavari-Ramshe & B. Ataie-Ashtiani https://doi.org/10.1007/s10346-015-0662-6
9. A two-layer shallow flow model with two axes of integration, well-balanced discretization and application to submarine avalanches J. Delgado-Sánchez et al. https://doi.org/10.1016/j.jcp.2019.109186
10. Experimental Study of the Effects of Selected Parameters on the Wavelength of Waves Generated by Landslide Masses S. Rostambeigi et al. https://doi.org/10.1007/s42286-026-00142-z
11. Modelling of long waves generated by bottom-tilting wave maker H. Lu et al. https://doi.org/10.1016/j.coastaleng.2017.01.007
12. Numerical modeling of the June 24, 2015, Hongyanzi landslide generated impulse waves in Three Gorges Reservoir, China L. Xiao et al. https://doi.org/10.1007/s10346-018-1057-2
13. Modeling landslide generated waves using the discontinuous finite element method W. Pan et al. https://doi.org/10.1002/fld.5090
14. Numerical Modeling of Tsunamis Generated by Subaerial, Partially Submerged, and Submarine Landslides T. Takabatake & R. Takemoto https://doi.org/10.3390/geohazards5040054
15. On the application of Miyata-Choi-Camassa model to surface waves T. Zhang et al. https://doi.org/10.1007/s10236-024-01630-x
16. Deep neural network–based prediction of near-field time series for landslide-generated tsunamis: A surrogate framework for boundary input modeling T. Takabatake et al. https://doi.org/10.1016/j.oceaneng.2026.125445
17. Landslide tsunamis: Comparison between depth-averaged and Navier–Stokes models A. Paris et al. https://doi.org/10.1016/j.coastaleng.2021.104022
18. A new run-up algorithm based on local high-order analytic expansions G. Khakimzyanov et al. https://doi.org/10.1016/j.cam.2015.12.004
19. Tsunami Squares modelling of the 2015 June 24 Hongyanzi landslide generated river tsunami in Three Gorges Reservoir, China J. Wang et al. https://doi.org/10.1093/gji/ggy425
20. Nonlinear random wave fields within a Boussinesq system M. Flamarion & E. Pelinovsky https://doi.org/10.1016/j.physleta.2024.129677
21. Soliton dynamics and stability in the Boussinesq equation for shallow water applications K. Farooq et al. https://doi.org/10.3389/fphy.2025.1637491
22. Analytical and Rothe time-discretization method for a Boussinesq-type system over an uneven bottom L. Mejía & J. Grajales https://doi.org/10.1016/j.cnsns.2021.105951
23. Numerical modeling of subaerial and submarine landslide-generated tsunami waves—recent advances and future challenges S. Yavari-Ramshe & B. Ataie-Ashtiani https://doi.org/10.1007/s10346-016-0734-2
24. Water waves induced by a moving bottom with a prescribed variable velocity W. Zhao et al. https://doi.org/10.1063/5.0313985
25. Improved Boussinesq-type equations for spatially and temporally varying bottom S. Beji https://doi.org/10.1080/21664250.2018.1520796
26. Deep learning-based landslide tsunami run-up prediction from synthetic gage data M. Açıkkar & B. Aydın https://doi.org/10.1016/j.apor.2024.104360