POSSIBLE CONTRIBUTIONS OF CITIZEN SCIENCE FOR LANDSLIDE HAZARD ASSESSMENT

7 citations as recorded by crossref.

1. A CitSci app for landslide data collection S. Kocaman & C. Gokceoglu https://doi.org/10.1007/s10346-018-1101-2
2. A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey R. Can et al. https://doi.org/10.3390/app11114993
3. Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost) T. Kavzoglu & A. Teke https://doi.org/10.1007/s13369-022-06560-8
4. Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR) C. Juang et al. https://doi.org/10.1371/journal.pone.0218657
5. Derivation of earthquake-induced landslide distribution using aerial photogrammetry: the January 24, 2020, Elazig (Turkey) earthquake G. Karakas et al. https://doi.org/10.1007/s10346-021-01660-2
6. How to Include Crowd-Sourced Photogrammetry in a Geohazard Observatory—Case Study of the Giant’s Causeway Coastal Cliffs M. Jaud et al. https://doi.org/10.3390/rs14143243
7. Review article: Natural hazard risk assessments at the global scale P. Ward et al. https://doi.org/10.5194/nhess-20-1069-2020