scholarly journals Ensembled Machine Learning Model for Aviation Incident Risk Prediction

2020 ◽  
Vol 9 (3) ◽  
pp. 351-353
Keyword(s):  
Air Transportation ◽  
Machine Model ◽  
Probabilistic Choice ◽  
Safety Reporting ◽  
Machine Learning Model ◽  
Vector Machines ◽  
High Hazard ◽  
Proactive Security ◽  
Aviation Safety Reporting System ◽  
Hazard Level

With the fabulous development of air traffic request expected throughout the following two decades, the security of the air transportation framework is of expanding concern. In this paper, we encourage the "proactive security" worldview to expand framework wellbeing with an emphasis on anticipating the seriousness of strange flight occasions as far as their hazard levels. To achieve this objective, a prescient model should be created to look at a wide assortment of potential cases and measure the hazard related with the conceivable result. By using the episode reports accessible in the Aviation Safety Reporting System (ASRS), we construct a half breed model comprising of help vector machine and K-closest neighbor calculation to evaluate the hazard related with the result of each perilous reason. The proposed system is created in four stages. Initially, we classify all the occasions, in view of the degree of hazard related with the occasion result, into five gatherings: high hazard, decently high hazard, medium hazard, respectably medium hazard, and okay. Furthermore, a help vector machine model is utilized to find the connections between the occasion outline in text configuration and occasion result. In this application K-closest neighbors (KNN) and bolster vector machines (SVM) are applied to group the everyday nearby climate types In equal, knn calculation is utilized to highlights and occasion results subsequently improving the forecast. At long last, the forecast on hazard level order is stretched out to occasion level results through a probabilistic choice tree

scholarly journals Effectiveness of low-impact development for urban inundation risk mitigation under different scenarios: a case study in Shenzhen, China

2018 ◽  
Vol 18 (9) ◽  
pp. 2525-2536 ◽  
Author(s):  
Jiansheng Wu ◽  
Rui Yang ◽  
Jing Song
Keyword(s):  
Local Governments ◽  
Risk Mitigation ◽  
Low Impact Development ◽  
Green Roofs ◽  
Rapid Urbanization ◽  
Storm Water Management Model ◽  
Inundation Depth ◽  
High Hazard ◽  
Hazard Level ◽  
Hazard Levels

Abstract. The increase in impervious surfaces associated with rapid urbanization is one of the main causes of urban inundation. Low-impact development (LID) practices have been studied for mitigation of urban inundation. This study used a hydrodynamic inundation model, coupling SWMM (Storm Water Management Model) and IFMS-Urban (Integrated Flood Modelling System–Urban), to assess the effectiveness of LID under different scenarios and at different hazard levels. The results showed that LID practices can effectively reduce urban inundation. The maximum inundation depth was reduced by 3 %–29 %, average inundation areas were reduced by 7 %–55 %, and average inundation time was reduced by 0 %–43 % under the eight scenarios. The effectiveness of LID practices differed for the three hazard levels, with better mitigation of urban inundation at a low hazard level than at a high hazard level. Permeable pavement (PP) mitigated urban inundation better than green roofs (GRs) under the different scenarios and at different hazard levels. We found that more implementation area with LID was not necessarily more efficient, and the scenario of 10 % PP+10 % GR was more efficient for the study area than other scenarios. The results of this study can be used by local governments to provide suggestions for urban inundation control, disaster reduction, and urban renewal.

scholarly journals Distributed collaborative prediction: Results of the machine learning contest

2017 ◽  
Vol 36 (3) ◽  
pp. 267-269 ◽  
Author(s):  
Matt Hall ◽  
Brendon Hall
Keyword(s):  
Machine Learning ◽  
Leading Edge ◽  
Support Vector ◽  
Small Data ◽  
Machine Model ◽  
Data Set ◽  
Facies Classification ◽  
Machine Learning Model ◽  
Wireline Logs ◽  
Naive Approach

The Geophysical Tutorial in the October issue of The Leading Edge was the first we've done on the topic of machine learning. Brendon Hall's article ( Hall, 2016 ) showed readers how to take a small data set — wireline logs and geologic facies data from nine wells in the Hugoton natural gas and helium field of southwest Kansas ( Dubois et al., 2007 ) — and predict the facies in two wells for which the facies data were not available. The article demonstrated with 25 lines of code how to explore the data set, then create, train and test a machine learning model for facies classification, and finally visualize the results. The workflow took a deliberately naive approach using a support vector machine model. It achieved a sort of baseline accuracy rate — a first-order prediction, if you will — of 0.42. That might sound low, but it's not untypical for a naive approach to this kind of problem. For comparison, random draws from the facies distribution score 0.16, which is therefore the true baseline.

Parameters of Support Vector Machines Model Optimized Method Based on Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 1873-1876
Author(s):  
Yu Zhen Xie ◽  
Zhao Gang Wang ◽  
Xiao Wei Dai
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Support Vector Machine Model ◽  
Optimization Problems ◽  
Support Vector ◽  
Machine Model ◽  
Penalty Factor ◽  
Vector Machines ◽  
Cross Validation Error ◽  
Function Selection

In order to obtain more accurate parameters of support vector machine model, using genetic algorithm to optimize the parameters is an effective method. This paper analyzes the principle of support vector machine for regression, support vector machine kernel function selection, kernel parameters, penalty factor selection and adjustment methods, taking into account genetic algorithm is effective in solving optimization problems, proposed a method using genetic algorithm to optimize the parameters of support vector machine, which uses genetic algorithms to make cross-validation error minimized. The simulation results demonstrate the effectiveness of this method.

Evaluation of Driver's Cognitive Distracted State Considering the Ambient State of a Car

2019 ◽  
Vol 13 (1) ◽  
pp. 13-24
Author(s):  
Hiroaki Koma ◽  
Taku Harada ◽  
Akira Yoshizawa ◽  
Hirotoshi Iwasaki
Keyword(s):  
Machine Learning ◽  
Driving Simulator ◽  
Support Vector ◽  
Biometric Data ◽  
State Data ◽  
The Road ◽  
Machine Learning Model ◽  
Vector Machines ◽  
Car Driving ◽  
Machine Learning Models

The effectiveness of considering the ambient state of a driving car for evaluating the driver's cognitive distracted state is evaluated. In this article, Support Vector Machines and Random Forest, which are representative machine learning models, are applied. As input data for the machine learning model, in addition to a driver's biometric data and car driving data, an ambient state data of a driving car are used. The ambient state data of a driving car considered in this study are that of the preceding car and the shape of the road. Experiments using a driving simulator are conducted to evaluate the effectiveness of considering the ambient state of a driving car.

scholarly journals Evaluation of landslide hazard level on spatial pattern of Tanah Datar Regency

2018 ◽  
Vol 229 ◽  
pp. 03006
Author(s):  
Indang Dewata ◽  
Iswandi Umar
Keyword(s):  
Land Use ◽  
Spatial Pattern ◽  
Human Life ◽  
Landslide Hazard ◽  
Residential Areas ◽  
Scoring Method ◽  
High Hazard ◽  
Hazard Level ◽  
Moderate Hazard ◽  
Hazard Levels

Natural disasters have caused much harm to human life. Migration through disaster based planning is one way to minimize risks. The purpose of this study is to evaluate the level of landslide hazard in the planning of spatial pattern in Tanah Datar Regency. To determine the landslide hazard level using the scoring method through the overlay of a thematic map. Indicators used are slope, rainfall, soil type, geology, landform, and land use. Furthermore, evaluation of landslide hazard on spatial planning is determined by overlaying hazard level maps with land use plans in spatial pattern planning. The results showed that the 26% landslide hazard rate was a high danger level, 46% moderate hazard level, and 38% low hazard level. In addition, hazard level evaluation of spatial patterns resulted in approximately 37.3% planning for residential areas having high hazard levels. Therefore, it is necessary to revise the pattern of regional space and include the element of disaster in the planning of spatial pattern.

scholarly journals Temporal variability of damage potential on roads as a conceptual contribution towards a short-term avalanche risk simulation

2005 ◽  
Vol 5 (2) ◽  
pp. 235-242 ◽  
Author(s):  
A. Zischg ◽  
S. Fuchs ◽  
M. Keiler ◽  
J. Stötter
Keyword(s):  
Temporal Variability ◽  
Traffic Accidents ◽  
Traffic Density ◽  
Short Term ◽  
Damage Potential ◽  
Fatality Risk ◽  
High Hazard ◽  
Hazard Level ◽  
High Traffic Density

Abstract. The fatality risk caused by avalanches on road networks can be analysed using a long-term approach, resulting in a mean value of risk, and with emphasis on short-term fluctuations due to the temporal variability of both, the hazard potential and the damage potential. In this study, the approach for analysing the long-term fatality risk has been adapted by modelling the highly variable short-term risk. The emphasis was on the temporal variability of the damage potential and the related risk peaks. For defined hazard scenarios resulting from classified amounts of snow accumulation, the fatality risk was calculated by modelling the hazard potential and observing the traffic volume. The avalanche occurrence probability was calculated using a statistical relationship between new snow height and observed avalanche releases. The number of persons at risk was determined from the recorded traffic density. The method resulted in a value for the fatality risk within the observed time frame for the studied road segment. The long-term fatality risk due to snow avalanches as well as the short-term fatality risk was compared to the average fatality risk due to traffic accidents. The application of the method had shown that the long-term avalanche risk is lower than the fatality risk due to traffic accidents. The analyses of short-term avalanche-induced fatality risk provided risk peaks that were 50 times higher than the statistical accident risk. Apart from situations with high hazard level and high traffic density, risk peaks result from both, a high hazard level combined with a low traffic density and a high traffic density combined with a low hazard level. This provided evidence for the importance of the temporal variability of the damage potential for risk simulations on road networks. The assumed dependence of the risk calculation on the sum of precipitation within three days is a simplified model. Thus, further research is needed for an improved determination of the diurnal avalanche probability. Nevertheless, the presented approach may contribute as a conceptual step towards a risk-based decision-making in risk management.

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