Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy
By Peter M. Atkinson, R. Massari. 1998
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Hamid Reza Pourghasemi
- E. Kutlug Sahin
- Bahareh Kalantar
- Binh Thai Pham
- Seyed Amir Naghibi
- Ahmed M. Youssef
- Taskin Kavzoglu
- Ismail Colkesen
Citations in the corpus
- Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms (2018, background): “Logistic regression is one of the best-fitting models to explore the relationship between a dependent variable and a set of independent explanatory variables, which could be categorical, continuous, or binary variables [71,72].”
- Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression (2011, background): “…applications such as evaluation of soil liquefaction potential (Juang et al. 2001, 2002; Lai et al. 2006), assessment of landslide susceptibility (Atkinson and Massari 1998; Carrara 1983; Chung and Fabbri 2003), classification of intermittent and perennial streams (Bent and Steeves 2006; Olson…”
- Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues (2013, background): “, 2005, Yilmaz, 2009a), linear regression (Atkinson and Massari, 1998), fuzzy membership (Kanungo et al.”
- Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, background): “…P denotes the probability related to a certain observation, and z could be de fi ned as where b 0 denotes the intercept of the algorithm, b i is the coef fi cient representing the independent variables contribution X i , and n denotes the number of conditioning factors (Atkinson and Massari 1998).”
- Spatial prediction models for landslide hazards: review, comparison and evaluation (2005, unknown): “On the other hand, often multi-temporal inventories or precise knowledge on landslide age are lacking, and only the current distributional pattern can be used to identify susceptible areas (Atkinson and Massari, 1998; Ohlmacher and Davis, 2003).”