scholarly journals Potential Evaluation of Forest Road Trench Failure in a Mountainous Forest, Northern Iran

2021 ◽  
Vol 43 (1) ◽  
Author(s):  
Aghil Moradmand Jalali ◽  
Ramin Naghdi ◽  
Ismael Ghajar
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Frequency Ratio ◽  
Logistic Regression Model ◽  
Road Construction ◽  
Susceptibility Map ◽  
Repair Cost ◽  
Northern Iran ◽  
Validation Data ◽  
Forest Road

After road construction in steep and mountainous areas, there is always a risk for trench failure. Estimation of this probability before forest road design and construction is urgent. Besides, to decrease failures costs and risks, it is necessary to classify their occurrence probabilities and identify the factors affecting them. The present study compares three statistical models of logistic regression, frequency ratio, and maximum entropy. The robust one was applied to generate trench failures susceptibility map of forest roads of two watersheds in Northern Iran. Also, all failures repairing costs were estimated, and subsequently, all existing roads were surveyed in the study area, detecting 844 failures. Among the recorded failures, 591 random cases (70%) were used in modeling, and others (30%) were used as validation data. The digital layers, including failure locations, were prepared. Three failure susceptibility maps were simulated using the outputs of the mentioned methods in the GIS environment. The resulted maps combined with repair cost prices were analyzed to statistically evaluate the repair cost unit per meter of forest road and per square meter of failure. The results showed that the logistic regression model had an Area Under Curve (AUC) of 74.6% in identifying failure-sensitive areas. The probabilistic frequency ratio and Entropy models showed 68.2 and 65.5% accuracy, respectively. Based on the logistic regression model, the distance to faults and terrain slope factors had the highest effects on forest road trenches failures. According to the result, about 43.25% of the existing road network is located in »high« and »very high« risky areas. The estimated cost of regulating and profiling trenches and ditches along the existing roads was approximately 108,772 $/km.

scholarly journals A logistic regression model for predicting the occurrence of intense geomagnetic storms

2005 ◽  
Vol 23 (9) ◽  
pp. 2969-2974 ◽  
Author(s):  
N. Srivastava
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Geomagnetic Storms ◽  
Training Data ◽  
Validation Dataset ◽  
Model Parameters ◽  
Validation Data ◽  
Data Set ◽  
Logistic Regression Models

Abstract. A logistic regression model is implemented for predicting the occurrence of intense/super-intense geomagnetic storms. A binary dependent variable, indicating the occurrence of intense/super-intense geomagnetic storms, is regressed against a series of independent model variables that define a number of solar and interplanetary properties of geo-effective CMEs. The model parameters (regression coefficients) are estimated from a training data set which was extracted from a dataset of 64 geo-effective CMEs observed during 1996-2002. The trained model is validated by predicting the occurrence of geomagnetic storms from a validation dataset, also extracted from the same data set of 64 geo-effective CMEs, recorded during 1996-2002, but not used for training the model. The model predicts 78% of the geomagnetic storms from the validation data set. In addition, the model predicts 85% of the geomagnetic storms from the training data set. These results indicate that logistic regression models can be effectively used for predicting the occurrence of intense geomagnetic storms from a set of solar and interplanetary factors.

Independent predictors for functional outcome after drainage of chronic subdural hematoma identified using a logistic regression model

2020 ◽  
Vol 64 (2) ◽  
Author(s):  
Sotirios Katsigiannis ◽  
Christina Hamisch ◽  
Boris Krischek ◽  
Marco Timmer ◽  
Anastasios Mpotsaris ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Functional Outcome ◽  
Subdural Hematoma ◽  
Logistic Regression Model ◽  
Chronic Subdural Hematoma

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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