Assessing shallow landslide susceptibility by using the Generalized Additive Model: a case study

2018 ◽  
Vol 46 ◽  
pp. 115-121
Author(s):  
Carlotta Bartelletti ◽  
Yuri Galanti ◽  
Michele Barsanti ◽  
Roberto Giannecchini ◽  
Giacomo D'Amato Avanzi ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Shallow Landslide

Integrating multiple data sources in match-fixing warning systems

2018 ◽  
Vol 18 (5-6) ◽  
pp. 483-504 ◽  
Author(s):  
Marius Ötting ◽  
Roland Langrock ◽  
Christian Deutscher
Keyword(s):  
Generalized Additive Model ◽  
Additive Model ◽  
Ground Truth ◽  
Warning Systems ◽  
Volume Data ◽  
Detection Systems ◽  
Multiple Data ◽  
Log Normal ◽  
Betting Odds

Recent years have seen several match-fixing scandals in soccer. In order to avoid match-fixing, existing literature and fraud detection systems primarily focus on analysing betting odds provided by bookmakers. In our work, we suggest to not only analyse odds but also total volume placed on bets, thereby making use of more of the information available. As a case study for our method, we consider the second division in Italian soccer, Serie B, since for this league it has effectively been proven that some matches were fixed, such that to some extent we can ground truth our approach. For the betting volume data, we use a flexible generalized additive model for location, scale and shape (GAMLSS), with log-normal response, to account for the various complex patterns present in the data. For the betting odds, we use a GAMLSS with bivariate Poisson response to model the number of goals scored by both teams, and to subsequently derive the corresponding odds. We then conduct outlier detection in order to flag suspicious matches. Our results indicate that monitoring both betting volumes and betting odds can lead to more reliable detection of suspicious matches.

scholarly journals Comparison of Landslide Susceptibility Maps using Logistic Regression (LR) and Generalized Additive Model (GAM)

2019 ◽  
Vol 9 (18) ◽  
pp. 208-219 ◽  
Author(s):  
Zeinab Timoori Yansari ◽  
Seyed Reza Hosseinzadeh ◽  
Ataollah Kavian ◽  
Hamid Reza Pourghasemi ◽  
◽  
...  
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

scholarly journals Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan

2009 ◽  
Vol 66 (6) ◽  
pp. 1417-1424 ◽  
Author(s):  
Hiroto Murase ◽  
Hiroshi Nagashima ◽  
Shiroh Yonezaki ◽  
Ryuichi Matsukura ◽  
Toshihide Kitakado
Keyword(s):  
Environmental Factors ◽  
Water Temperature ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Distribution Patterns ◽  
Pelagic Fish ◽  
Environmental Covariates ◽  
Ammodytes Personatus ◽  
Sendai Bay

Abstract Murase, H., Nagashima, H., Yonezaki, S., Matsukura, R., and Kitakado, T. 2009. Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of pelagic fish and krill: a case study in Sendai Bay, Japan. – ICES Journal of Marine Science, 66: 1417–1424. A generalized additive model (GAM) was applied to fishery-survey data to reveal the influences of environmental factors on the distribution patterns of Japanese anchovy (Engraulis japonicus), sand lance (Ammodytes personatus), and krill (Euphausia pacifica). Echosounder and physical-oceanographic data were collected in Sendai Bay, Japan, in spring 2005. A hierarchical model was used with two spatial strata: (i) presence and absence of each species; and (ii) biomass density of each species, given its presence; and six environmental covariates (surface water temperature, salinity, and chlorophyll, and near-seabed water temperature, salinity, and depth). The results indicate non-linear responses of the two indices to the environmental covariates. In addition, the biomasses estimated by the GAMs were comparable with estimates based on conventional, stratified-random sampling for each species. GAMs are very useful for (i) investigating the effects of environmental factors on the distributions of biological organisms, and (ii) predicting the distributions of animal densities in unsurveyed areas.

Sign in / Sign up

Export Citation Format

Share Document