47 citations as recorded by crossref.

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5. Satellite-derived rainfall thresholds for landslide early warning in Bogowonto Catchment, Central Java, Indonesia E. Chikalamo et al. https://doi.org/10.1016/j.jag.2020.102093
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7. Quantitative Risk Assessment of Road Networks Subject to Natural Hazards: A State-of-the-Art Review A. Roy et al. https://doi.org/10.1061/JSDCCC.SCENG-1812
8. The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations G. Skofronick‐Jackson et al. https://doi.org/10.1002/qj.3313
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11. Spatially distributed rainfall information and its potential for regional landslide early warning systems E. Canli et al. https://doi.org/10.1007/s11069-017-2953-9
12. A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia C. Cullen et al. https://doi.org/10.3390/rs14092239
13. Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China C. Zhu et al. https://doi.org/10.1007/s11069-019-03806-x
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18. A Deformable Dual-Branch Visual State-Space Network for Landslide Identification with Multi-Scale Recognition and Irregular Boundary Enhancement B. Du et al. https://doi.org/10.3390/rs18050707
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23. Data-Driven Landslide Nowcasting at the Global Scale T. Stanley et al. https://doi.org/10.3389/feart.2021.640043
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26. Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era T. Stanley et al. https://doi.org/10.1175/EI-D-16-0025.1
27. Global Assessment of the Capability of Satellite Precipitation Products to Retrieve Landslide-Triggering Extreme Rainfall Events O. Marc et al. https://doi.org/10.1175/EI-D-21-0022.1
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34. Incorporation of Satellite Precipitation Uncertainty in a Landslide Hazard Nowcasting System S. Hartke et al. https://doi.org/10.1175/JHM-D-19-0295.1
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36. Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog C. Dandridge et al. https://doi.org/10.3390/su15043323
37. A susceptibility-based rainfall threshold approach for landslide occurrence E. Monsieurs et al. https://doi.org/10.5194/nhess-19-775-2019
38. Patterns of influence of different landslide boundaries and their spatial shapes on the uncertainty of landslide susceptibility prediction Y. Xing et al. https://doi.org/10.1007/s11069-023-06025-7
39. Evaluating TMPA Rainfall over the Sparsely Gauged East African Rift E. Monsieurs et al. https://doi.org/10.1175/JHM-D-18-0103.1
40. A new emergency decision support system: the automatic interpretation and contextualisation of events to model a crisis situation in real-time A. Fertier et al. https://doi.org/10.1016/j.dss.2020.113260
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42. Machine learning and landslide studies: recent advances and applications F. Tehrani et al. https://doi.org/10.1007/s11069-022-05423-7
43. A systematic review on rainfall thresholds for landslides occurrence F. Gonzalez et al. https://doi.org/10.1016/j.heliyon.2023.e23247
44. The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides I. Krøgli et al. https://doi.org/10.5194/nhess-18-1427-2018
45. Spatiotemporal warning of rainfall-induced railway geohazards in China B. Li et al. https://doi.org/10.1016/j.enggeo.2025.108234
46. NASA’s Global Precipitation Measurement Mission: Leveraging Stakeholder Engagement & Applications Activities to Inform Decision-making A. Portier et al. https://doi.org/10.1016/j.rsase.2022.100853
47. Using satellite rainfall products to assess the triggering conditions for hydro-morphological processes in different geomorphological settings in China N. Wang et al. https://doi.org/10.1016/j.jag.2021.102350