64 citations as recorded by crossref.

1. Comparison of Different Estimation Approaches in Rare Events Data E. BACAKSIZ & S. KOÇ https://doi.org/10.21121/eab.960840
2. Gully erosion mapping based on hydro-geomorphometric factors and geographic information system K. Shirani et al. https://doi.org/10.1007/s10661-023-11197-7
3. Inference of spatiotemporal effects on cellular state transitions from time-lapse microscopy M. Strasser et al. https://doi.org/10.1186/s12918-015-0208-5
4. Assessing the risk of windshear occurrence at HKIA using rare‐event logistic regression F. Chen et al. https://doi.org/10.1002/met.1962
5. Machine Learning-Based Water Quality Classification Assessment W. Chen et al. https://doi.org/10.3390/w16202951
6. A Landslide Numerical Factor Derived from CHIRPS for Shallow Rainfall Triggered Landslides in Colombia C. Cullen et al. https://doi.org/10.3390/rs14092239
7. Do food safety certifications improve the safety of our food system? evidence from the U.S. Meat, Poultry, and egg industry L. Hu & Y. Zheng https://doi.org/10.1016/j.foodpol.2025.102980
8. Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France) M. Fressard et al. https://doi.org/10.5194/nhess-14-569-2014
9. A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer V. Allocca et al. https://doi.org/10.1016/j.scitotenv.2021.148067
10. Impact of tourism development on the local livelihoods and land cover change in the Northern Vietnamese highlands T. Hoang et al. https://doi.org/10.1007/s10668-018-0253-5
11. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model H. Hussin et al. https://doi.org/10.1016/j.geomorph.2015.10.030
12. Forest cover change trajectories and their impact on landslide occurrence in the tropical Andes M. Guns & V. Vanacker https://doi.org/10.1007/s12665-013-2352-9
13. Daily prediction modeling of forest fire ignition using meteorological drought indices in the Mexican highlands A. Vilchis-Francés et al. https://doi.org/10.3832/ifor3623-014
14. Prediction model for cyanide soil pollution in artisanal gold mining area by using logistic regression L. Razanamahandry et al. https://doi.org/10.1016/j.catena.2017.11.018
15. Predicting the susceptibility to gully initiation in data-poor regions O. Dewitte et al. https://doi.org/10.1016/j.geomorph.2014.08.010
16. Travel distance estimation of landslide-induced debris flows by machine learning method in Nepal Himalaya after the Gorkha earthquake C. Qiu & X. Geng https://doi.org/10.1007/s10064-024-03883-8
17. Study on spatial tropism distribution of rural settlements in the Loess Hilly and Gully Region based on natural factors and traffic accessibility Z. Chen et al. https://doi.org/10.1016/j.jrurstud.2019.02.014
18. Tuberculosis in Myasthenia Gravis patients on immunosuppressive therapy in a high-risk area: Implications for preventative therapy E. Steyn et al. https://doi.org/10.1016/j.jns.2021.117447
19. Rainfall-Induced Landslide Susceptibility Using a Rainfall–Runoff Model and Logistic Regression H. Chan et al. https://doi.org/10.3390/w10101354
20. Logistic regression in large rare events and imbalanced data: A performance comparison of prior correction and weighting methods M. Maalouf et al. https://doi.org/10.1111/coin.12123
21. Modeling the Impact of Spatial Correlations of Road Failures on Travel Times during Adverse Weather Conditions W. Wisetjindawat et al. https://doi.org/10.1177/0361198119844251
22. Quantifying the risk of mass shootings at specific locations X. Lei & C. MacKenzie https://doi.org/10.1111/risa.14197
23. Living with landslides: Land use on unstable hillslopes in a rural tropical mountainous environment in DR Congo J. Maki Mateso et al. https://doi.org/10.1016/j.scitotenv.2024.171624
24. Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression M. Mărgărint et al. https://doi.org/10.5194/nhess-13-3339-2013
25. Landslide susceptibility assessment for the Darjeeling Toy Train route: a GIS and machine learning approach P. Sarkar et al. https://doi.org/10.1007/s00477-024-02885-y
26. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China H. Hong et al. https://doi.org/10.1016/j.scitotenv.2017.12.256
27. Assessment of rainfall-induced shallow landslides in Phetchabun and Krabi provinces, Thailand K. Ono et al. https://doi.org/10.1007/s11069-014-1292-3
28. Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model Y. Ren et al. https://doi.org/10.1016/j.aap.2016.07.017
29. Effectiveness of machine learning ensemble models in assessing groundwater potential in Lidder watershed, India R. Ali et al. https://doi.org/10.1007/s11600-023-01237-8
30. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai https://doi.org/10.1016/j.enggeo.2018.07.019
31. Issuer Motivations for Corporate Green Bond Offerings M. Dutordoir et al. https://doi.org/10.1111/1467-8551.12747
32. Exploring the associated factors for multiple-threats and near-miss incidents at signalized midblock crosswalks B. Kutela & H. Teng https://doi.org/10.1080/19439962.2019.1638476
33. A statistical scheme to forecast the daily lightning threat over southern Africa using the Unified Model M. Gijben et al. https://doi.org/10.1016/j.atmosres.2017.04.022
34. Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments W. Chen et al. https://doi.org/10.3390/rs12233854
35. Assessing the quality of landslide susceptibility maps – case study Lower Austria H. Petschko et al. https://doi.org/10.5194/nhess-14-95-2014
36. Do denser forests have greater risk of tree mortality: A remote sensing analysis of density-dependent forest mortality K. Van Gunst et al. https://doi.org/10.1016/j.foreco.2015.09.032
37. Introducing a novel multi-layer perceptron network based on stochastic gradient descent optimized by a meta-heuristic algorithm for landslide susceptibility mapping H. Hong et al. https://doi.org/10.1016/j.scitotenv.2020.140549
38. In Silico Models for Design and Optimization of Science-Based Listeria Environmental Monitoring Programs in Fresh-Cut Produce Facilities G. Sullivan et al. https://doi.org/10.1128/AEM.00799-21
39. Impact of real-time traffic characteristics on crash occurrence: Preliminary results of the case of rare events A. Theofilatos et al. https://doi.org/10.1016/j.aap.2017.12.018
40. Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping P. Nguyen et al. https://doi.org/10.3390/app10072469
41. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China P. Tsangaratos et al. https://doi.org/10.1007/s10346-016-0769-4
42. Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China T. Liu et al. https://doi.org/10.1109/JSTARS.2021.3117975
43. Comparison of spatial and aspatial logistic regression models for landmine risk mapping C. Schultz et al. https://doi.org/10.1016/j.apgeog.2015.11.005
44. Predicting Road Accidents: A Rare-events Modeling Approach A. Theofilatos et al. https://doi.org/10.1016/j.trpro.2016.05.293
45. The effect of geomorphic and anthropogenic factors on the karst spring occurrence (case studies of central Zagros Mountain Range, Iran) M. Ghadimi et al. https://doi.org/10.1007/s11600-025-01543-3
46. Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows T. Heckmann et al. https://doi.org/10.5194/nhess-14-259-2014
47. Natural spring-based water potential in Bangladesh using ensemble machine learning algorithms S. Hosan et al. https://doi.org/10.2166/wcc.2026.210
48. Determinants of Firms’ Choice between Green and Conventional Bonds: Why is the Corporate Green Bond Market Still so ‘Green’? M. Dutordoir et al. https://doi.org/10.2139/ssrn.4156431
49. Simulating the effects of weather and climate on large wildfires in France R. Barbero et al. https://doi.org/10.5194/nhess-19-441-2019
50. Crash proneness? Predictors of repeat crashes in older drivers B. Huang et al. https://doi.org/10.1016/j.jsr.2021.10.003
51. Guidance Index for Shallow Landslide Hazard Analysis C. Avalon Cullen et al. https://doi.org/10.3390/rs8100866
52. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China H. Hong et al. https://doi.org/10.1016/j.scitotenv.2018.02.278
53. Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods W. Chen et al. https://doi.org/10.1016/j.scitotenv.2019.05.312
54. Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy) M. Cama et al. https://doi.org/10.1007/s12665-015-5047-6
55. Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran K. Shirani et al. https://doi.org/10.1007/s11069-018-3356-2
56. A review of the literature on spatial prediction of floods using machine learning models S. Hajji et al. https://doi.org/10.1007/s11852-025-01158-2
57. Multi‐stemming and size enhance survival of dominant tree species in a frequently typhoon‐disturbed forest S. Su et al. https://doi.org/10.1111/jvs.12858
58. Environmental, climatic, and situational factors influencing the probability of fatality or injury occurrence in flash flooding: a rare event logistic regression predictive model S. Chang et al. https://doi.org/10.1007/s11069-023-05845-x
59. The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model M. Jafarzadeh et al. https://doi.org/10.52547/jwmr.11.22.96
60. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size P. Tsangaratos & I. Ilia https://doi.org/10.1016/j.catena.2016.06.004
61. A Comparative Analysis of Binary Logistic Regression and Analytical Hierarchy Process for Landslide Susceptibility Assessment in the Dobrov River Basin, Romania C. PATRICHE et al. https://doi.org/10.1016/S1002-0160(15)60047-9
62. Performance of prior and weighting bias correction methods for rare event logistic regression under the influence of sampling bias O. Alpay & E. Çankaya https://doi.org/10.1080/03610918.2021.1872629
63. Robust variable selection in the logistic regression model Y. JIANG et al. https://doi.org/10.15672/hujms.810383
64. Landslide integrated characteristics and susceptibility assessment in Rongxian county of Guangxi, China L. Liao et al. https://doi.org/10.1007/s11629-017-4804-2