scholarly journals Applying logistic regression method to determine combinatorial optimization of landslide-related factors and construct landslide hazard map in Khanh Vinh district, Khanh Hoa Province

2017 ◽  
Vol 20 (K4) ◽  
pp. 76-83
Author(s):  
Danh Thanh Nguyen ◽  
Ngo Van Dau ◽  
Dung Quoc Ta
Keyword(s):  
Logistic Regression ◽  
Combinatorial Optimization ◽  
Regression Method ◽  
Landslide Hazard ◽  
Optimization Models ◽  
Hazard Map ◽  
Related Factors ◽  
Maximum Precipitation ◽  
Logistic Regression Method ◽  
Landslide Hazard Map

The purpose of this study is to produce landslide hazard map in Khanh Vinh district, Khanh Hoa province using logistic regression method integrated with GIS analytical tools. The spatial relationship between landslide-related factors such as topography; lithology; vegetation; maximum precipitation in year; distance from roads; distance from drainages; distance from faults and the distribution of landslides were used in the landslide hazard analyses. Using success rate and prediction rate curve assess the fit and accuracy of logistic regression method. The results show that this method have the goodness of fit and the high accuracy (Areas Under Curves - AUC = 0.8 ~ 0.9). Bayesian Model Average (BMA) of the R statistical software was applied to identify the most influential factors and the combinatorial optimization models of landslide-related factors. There are four the most important landslide-related factors and five combinatorial optimization models of landslide-related factors. Model 3 (slope angle, slope aspect, altitude, distance from roads and maximum precipitation in year) is the best optimization.

scholarly journals Landslide hazard zonation using logistic regression technique: the case of Shafe and Baso catchments, Gamo highland, Ethiopia

2021 ◽  
Author(s):  
Leulalem Shano ◽  
Tarun Kumar Raghuvanshi ◽  
Matebie Meten
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Hazard Map ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Landslide Hazard Map ◽  
Very High

Abstract Landslide hazard zonation plays an important role in safe and viable infrastructure development, urbanization, land use, and environmental planning. The Shafe and Baso catchments are found in the Gamo highland which has been highly degraded by erosion and landslides thereby affecting the lives of the local people. In recent decades, recurrent landslide incidences were frequently occurring in this Highland region of Ethiopia in almost every rainy season. This demands landslide hazard zonation in the study area in order to alleviate the problems associated with these landslides. The main objectives of this study are to identify the spatiotemporal landslide distribution of the area; evaluate the landslide influencing factors and prepare the landslide hazard map. In the present study, lithology, groundwater conditions, distance to faults, morphometric factors (slope, aspect and curvature), and land use/land cover were considered as landslide predisposing/influencing factors while precipitation was a triggering factor. All these factor maps and landslide inventory maps were integrated using ArcGIS 10.4 environment. For data analysis, the principle of logistic regression was applied in a statistical package for social sciences (SPSS). The result from this statistical analysis showed that the landslide influencing factors like distance to fault, distance to stream, groundwater zones, lithological units and aspect have revealed the highest contribution to landslide occurrence as they showed greater than a unit odds ratio. The resulting landslide hazard map was divided into five classes: very low (13.48%), low (28.67%), moderate (31.62%), high (18%), and very high (8.2%) hazard zones which was then validated using the goodness of fit techniques and receiver operating characteristic curve (ROC) with an accuracy of 85.4. The high and very high landslide hazard zones should be avoided from further infrastructure and settlement planning unless proper and cost-effective landslide mitigation measures are implemented.

scholarly journals Landslide hazard assessment around MCT zone in Marsyangdi River basin, west Nepal

2017 ◽  
Vol 53 ◽  
pp. 93-98
Author(s):  
Subash Acharya ◽  
Dinesh Pathak
Keyword(s):  
Logistic Regression ◽  
River Basin ◽  
Hazard Assessment ◽  
Landslide Hazard ◽  
Slope Aspect ◽  
Hazard Map ◽  
Landslide Hazard Assessment ◽  
Hazard Zone ◽  
High Hazard ◽  
Landslide Hazard Map

In the hilly and mountainous terrain of Nepal, landslide is the most common natural hazard especially during prolong rainfall. Every year landslide cost lives and causes injuries. In order to address this problem, the best that can be done is to prepare the landslide hazard map of the area, apply mitigation measures and evacuate the high hazardous area, if necessary. Landslide hazard assessment is the primary tool so as to understand the nature and characteristics of the slope that are prone to failure. Logistic Regression Model is used for the preparation of landslide hazard map of the Besi Shahar-Tal area in Marsyangdi River basin in west Nepal. The causative factors such as elevation, slope, slope aspect, land use, geology, rainfall, lineament density, stream density are used. All the thematic layers of these parameters are prepared in GIS and logistic regression analysis is done by using Statistical Package for Social Science (SPSS). Five different hazard zones are separated namely very low hazard zone, low hazard zone, medium hazard zone, high hazard zone and very high hazard zone. The high hazard zone is lying along the Marsyangdi River and its tributaries.

scholarly journals Prediksi Not Operational Transaction Menggunakan Logistic Regression pada Bank XYZ di Kota Kupang

AITI ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 42-55
Author(s):  
Radius Tanone ◽  
Arnold B Emmanuel
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Method ◽  
Learning Approach ◽  
Know How ◽  
Independent Variables ◽  
Machine Learning Approach ◽  
Logistic Regression Method ◽  
Python Programming

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.  

scholarly journals Talent Performance Analysis Using People Analytics Approach

2021 ◽  
Vol 4 (1) ◽  
pp. 216-230
Author(s):  
Fahreza Nasril ◽  
Dian Indiyati ◽  
Gadang Ramantoko
Keyword(s):  
Logistic Regression ◽  
Multinomial Logistic Regression ◽  
Research Question ◽  
Development Program ◽  
Individual Performance ◽  
Study Data ◽  
Regression Method ◽  
Process Data ◽  
Logistic Regression Method ◽  
The Right

The purpose of this study was to answer the research question "How is the prediction of Talent Performance in the following year with the application of People Analytics?" and knowing the description of employees who are potential talents, the resulting performance contributions, to the description of the development and retention efforts needed by Talent in order to be able to maintain their future performance and position as Talents compared to the previous People Analytics method using predictive analysis, namely prediction of Talent Performance in the year next. In this study, data analysis using the Multivariate Logistic Regression method is used to get the Prediction of the Performance of Talents who become the object of research in the form of individual performance quickly and precisely in accordance with the patterns drawn by individual Performance score data in previous years. And can provide insight regarding the projected strategies that need to be done to maintain the improvement of individual talent performance in the years of the assessment period. It also helps management in making decisions about the right Talent development program and determining which Talents are priorities. The population in this study were the talents of employees of PT. Angkasa Pura II (Persero) with a managerial level consisting of: Senior Leader, Middle Leader, and First Line Leader who has a Person Grade (PG) range of 13 to 21. The sample used is Middle Leader level talent with specified criteria and through a process data cleansing. The results of this study indicate that the variable that significantly affects the performance of the following year is the performance of the previous 2 years. Then prediction analysis can be done using these independent variables with the Multinomial Logistic Regression method, and to get prediction results with better accuracy can be done by the Random Forest method.

scholarly journals Prognostic algorithms for the progression of chronic heart failure depending on the clinical phenotype

2019 ◽  
Vol 18 (3) ◽  
pp. 41-47
Author(s):  
E. A. Polunina ◽  
L. P. Voronina ◽  
E. A. Popov ◽  
I. S. Belyakova ◽  
O. S. Polunina ◽  
...  
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Chronic Heart Failure ◽  
Clinical Phenotype ◽  
Left Ventricular ◽  
Regression Method ◽  
Left Ventricular Ejection ◽  
Control Group ◽  
Ventricular Ejection Fraction ◽  
Logistic Regression Method

Aim. To develop a mathematical equation (algorithm) to predict the development of chronic heart failure (CHF) for three years, depending on the clinical phenotype.Material and methods. Three hundred forty five patients with CHF with a different left ventricular ejection fraction (preserved, mean, low) were examined. The control group included somatically healthy individuals (n=60). In all patients, 48 parameters that most widely characterize the pathogenesis of CHF (gender-anamnestic, clinical, instrumental, biochemical) were analyzed. To isolate phenotypes, dispersive and cluster analysis was used: the hierarchical classification method and the k-means method. In the development of algorithms we used binary logistic regression method. We used ROC curve to assess the quality of the obtained algorithms.Results. We identified four phenotypes in patients with CHF: fibro-rigid, fibro-inflammatory, inflammatory-destructive, dilated-maladaptive. For the first three phenotypes, a mathematical logistic regression method was used to develop mathematical models for predicting the progression of CHF for three years, with the release of predictors for each phenotype. Belonging to the dilatedmaladaptive phenotype according to the results of the analysis is already an indicator of an unfavorable prognosis in patients with CHF.Conclusion. The developed algorithms based on the selected phenotypes have high diagnostic sensitivity and specificity and can be recommended for use in clinical practice.

scholarly journals Dendrochronological dating as the basis for developing a landslide hazard map – An example from the Western Carpathians, Poland

2018 ◽  
Vol 45 (1) ◽  
pp. 173-184 ◽  
Author(s):  
Katarzyna Łuszczyńska ◽  
Małgorzata Wistuba ◽  
Ireneusz Malik ◽  
Marek Krąpiec ◽  
Bartłomiej Szypuła
Keyword(s):  
Low Cost ◽  
Sampling Site ◽  
Ring Width ◽  
Landslide Hazard ◽  
Hazard Map ◽  
Large Area ◽  
Landslide Occurrence ◽  
Landslide Activity ◽  
Landslide Event ◽  
Landslide Hazard Map

Abstract Most landslide hazard maps are developed on the basis of an area’s susceptibility to a landslide occurrence, but dendrochronological techniques allows one to develop maps based on past landslide activity. The aim of the study was to use dendrochronological techniques to develop a landslide hazard map for a large area, covering 3.75 km2. We collected cores from 131 trees growing on 46 sampling sites, measured tree-ring width, and dated growth eccentricity events (which occur when tree rings of different widths are formed on opposite sides of a trunk), recording the landslide events which had occurred over the previous several dozen years. Then, the number of landslide events per decade was calculated at every sampling site. We interpolated the values obtained, added layers with houses and roads, and developed a landslide hazard map. The map highlights areas which are potentially safe for existing buildings, roads and future development. The main advantage of a landslide hazard map developed on the basis of dendrochronological data is the possibility of acquiring long series of data on landslide activity over large areas at a relatively low cost. The main disadvantage is that the results obtained relate to the measurement of anatomical changes and the macroscopic characteristics of the ring structure occurring in the wood of tilted trees, and these factors merely provide indirect information about the time of the landslide event occurrence.

Effects of a Bolster Curvature on an Automobile Seat Fit

2021 ◽  
Vol 65 (1) ◽  
pp. 423-426
Author(s):  
Baekhee Lee ◽  
Byoung-Keon (Daniel) Park ◽  
Kihyo Jung ◽  
Jangwoon Park
Keyword(s):  
Logistic Regression ◽  
Design Process ◽  
Cross Sections ◽  
Classification Accuracy ◽  
Regression Method ◽  
Digital Design ◽  
Cross Sectional ◽  
Development Costs ◽  
Logistic Regression Method ◽  
Vehicle Seat

Vehicle-seat dimensions measured at specific cross-sections have been historically utilized as shape determinants to evaluate a driver’s seat fit. The present study is intended to quantify the relationships between seat fits and the seat dimensions for designing an ergonomic vehicle seat. Eight seat engineers evaluated seat fits for 54 different driver seats based on their expertise. Five seat dimensions were measured at six cross-sectional planes using a custom-built, computerized program. The best-subset-logistic-regression method was employed to model the relationships between the seat fit and the seat dimensions. As a result, significant seat dimensions, such as insert width, bolster height, and/or bolster curvature, on the subjective seat fit (e.g., loose-fit, right-fit, and tight-fit) were quantified. The developed models showed 98% overall classification accuracy throughout the cross-sectional planes. The models promote a digital design process of an automobile seat, which would increase the efficiency of the process and reduce the development costs.

Modeling Species Distribution

2011 ◽  
pp. 171-197 ◽  
Author(s):  
Yongyut Trisurat ◽  
Albertus G. Toxopeus
Keyword(s):  
Logistic Regression ◽  
Maximum Entropy ◽  
Habitat Suitability ◽  
Logistic Regression Model ◽  
Entropy Method ◽  
Regression Method ◽  
Logistic Regression Method ◽  
Suitable Habitats ◽  
Suitable Area ◽  
Available Information

The results show that among the three approaches, the potentially suitable habitats derived from cartographic overlay cover the largest area and are likely to overestimate existing occurrence areas. The logistic regression model predicts approximately 56% as suitable area, while maximum entropy results covers approximately 9% of the sanctuary. Although the results show large differences in the suitable areas, it should not be concluded that any one method always proves better than the others. Utilization of any method is dependent on the situation and available information. If species observations are limited, the cartographic overlay or habitat suitability is recommended. The logistic regression method is recommended when adequate presence and absence data are available. If presence-only data is available, a niche-based model or the maximum entropy method (MAXENT) is highly recommended.

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