65 citations as recorded by crossref.

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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
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8. 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
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10. 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
11. 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
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13. 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
14. 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
15. 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
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17. 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
18. 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
19. 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
20. Rainfall-Induced Landslide Susceptibility Using a Rainfall–Runoff Model and Logistic Regression H. Chan et al. https://doi.org/10.3390/w10101354
21. 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
22. 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
23. Quantifying the risk of mass shootings at specific locations X. Lei & C. MacKenzie https://doi.org/10.1111/risa.14197
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai https://doi.org/10.1016/j.enggeo.2018.07.019
32. Issuer Motivations for Corporate Green Bond Offerings M. Dutordoir et al. https://doi.org/10.1111/1467-8551.12747
33. 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
34. 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
35. Combining Evolutionary Algorithms and Machine Learning Models in Landslide Susceptibility Assessments W. Chen et al. https://doi.org/10.3390/rs12233854
36. Assessing the quality of landslide susceptibility maps – case study Lower Austria H. Petschko et al. https://doi.org/10.5194/nhess-14-95-2014
37. 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
38. 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
39. 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
40. 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
41. Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping P. Nguyen et al. https://doi.org/10.3390/app10072469
42. 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
43. 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
44. 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
45. Predicting Road Accidents: A Rare-events Modeling Approach A. Theofilatos et al. https://doi.org/10.1016/j.trpro.2016.05.293
46. 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
47. 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
48. Natural spring-based water potential in Bangladesh using ensemble machine learning algorithms S. Hosan et al. https://doi.org/10.2166/wcc.2026.210
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. Machine learning-based water quality index classification: A case study of the upper reaches of the Hau River, Vietnam T. Ngoc et al. https://doi.org/10.1016/j.envc.2026.101577
62. 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
63. 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
64. Robust variable selection in the logistic regression model Y. JIANG et al. https://doi.org/10.15672/hujms.810383
65. 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