• The Journal of Engineering Geology
• /
• v.30 no.3
• /
• pp.315-325
• /
• 2020

This study was performed to develop a model to predict landslides and determine the variable importance of landslides susceptibility factors based on the probabilistic prediction of landslides occurring on slopes along the road. Field survey data of 30,615 slopes from 2007 to 2020 in Korea were analyzed to develop a landslide prediction model. Of the total 131 variable factors, 17 topographic factors and 114 geological factors (including 89 bedrocks) were used to predict landslides. Automated machine learning (AutoML) was used to classify landslides and non-landslides. The verification results revealed that the best model, an extremely randomized tree (XRT) with excellent predictive performance, yielded 83.977% of prediction rates on test data. As a result of the analysis to determine the variable importance of the landslide susceptibility factors, it was composed of 10 topographic factors and 9 geological factors, which was presented as a percentage for each factor. This model was evaluated probabilistically and quantitatively for the likelihood of landslide occurrence by deriving the ranking of variable importance using only on-site survey data. It is considered that this model can provide a reliable basis for slope safety assessment through field surveys to decision-makers in the future.

• Convergence Security Journal
• /
• v.23 no.2
• /
• pp.37-45
• /
• 2023

Cyber deception technology plays a crucial role in monitoring attacker activities and detecting new types of attacks. However, along with the advancements in deception technology, the development of Anti-honeypot technology has allowed attackers who recognize the deceptive environment to either cease their activities or exploit the environment in reverse. Currently, deception technology is unable to identify or respond to such situations. In this study, we propose a predictive model using Markov chain analysis to determine the identification of attackers who infiltrate deceptive environments. The proposed model for deception status determination is the first attempt of its kind and is expected to overcome the limitations of existing deception-based attacker analysis, which does not consider attackers who identify the deceptive environment. The classification model proposed in this study demonstrated a high accuracy rate of 97.5% in identifying and categorizing attackers operating in deceptive environments. By predicting the identification of an attacker's deceptive environment, it is anticipated that this model can provide refined data for numerous studies analyzing deceptive environment intrusions.

• Design Convergence Study
• /
• v.15 no.6
• /
• pp.195-214
• /
• 2016

Predictive analysis is based on a probabilistic learning algorithm called pattern recognition or machine learning. Therefore, if users want to extract more information from the data, they are required high statistical knowledge. In addition, it is difficult to find out data pattern and characteristics of the data. This study conducted statistical data analyses and visual data analyses to supplement prediction analysis's weakness. Through this study, we could find some implications that haven't been found in the previous studies. First, we could find data pattern when adjust data selection according as splitting criteria for the decision tree method. Second, we could find what type of data included in the final prediction model. We found some implications that haven't been found in the previous studies from the results of statistical and visual analyses. In statistical analysis we found relation among the multivariable and deducted prediction model to predict high box office performance. In visualization analysis we proposed visual analysis method with various interactive functions. Finally through this study we verified final prediction model and suggested analysis method extract variety of information from the data.

• Food Science of Animal Resources
• /
• v.36 no.6
• /
• pp.752-759
• /
• 2016

The objective of this study was to describe the growth patterns of Staphylococcus aureus in combinations of NaCl and $NaNO_2$, using a probabilistic model. A mixture of S. aureus strains (NCCP10826, ATCC13565, ATCC14458, ATCC23235, and ATCC27664) was inoculated into nutrient broth plus NaCl (0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, and 1.75%) and $NaNO_2$ (0, 15, 30, 45, 60, 75, 90, 105, and 120 ppm). The samples were then incubated at 4, 7, 10, 12 and $15^{\circ}C$ for up to 60 d under aerobic or vacuum conditions. Growth responses [growth (1) or no growth (0)] were then determined every 24 h by turbidity, and analyzed to select significant parameters (p<0.05) by a stepwise selection method, resulting in a probabilistic model. The developed models were then validated with observed growth responses. S. aureus growth was observed only under aerobic storage at $10-15^{\circ}C$. At $10-15^{\circ}C$, NaCl and $NaNO_2$ did not inhibit S. aureus growth at less than 1.25% NaCl. Concentration dependency was observed for NaCl at more than 1.25%, but not for $NaNO_2$. The concordance percentage between observed and predicted growth data was approximately 93.86%. This result indicates that S. aureus growth can be inhibited in vacuum packaging and even aerobic storage below $10^{\circ}C$. Furthermore, $NaNO_2$ does not effectively inhibit S. aureus growth.

• Journal of Soil and Groundwater Environment
• /
• v.22 no.5
• /
• pp.30-39
• /
• 2017

In this study, the predictive toxicity of barley Hordeum vulgare was estimated using a modified terrestrial biotic ligand model (TBLM) to account for the toxic effects of $CuOH^+$ and $CuCO_3(aq)$ generated at pH 7 or higher, and this was compared to that from the original TBLM. At pH values higher than 7, the difference in $EA_{50}\{Cu^{2+}\}$ (half maximal effective activity of $Cu^{2+}$) between the two models increased with increasing pH. As Mg concentration increased from 8.24 to 148 mg/L in the pH range of 5.5 to 8.5, the difference in $EA_{50}\{Cu^{2+}\}$ increased, and it reached its maximum at pH 8. The difference in $EC_{50}[Cu]_T$ (half maximal effective concentration of Cu) between the two models increased as dissolved organic carbon (DOC) concentration increased when pH was above 7. Thus, for soils with alkaline pH, the toxic effect of $CuOH^+$ and $CuCO_3(aq)$ are greater at higher salt and DOC concentrations. The acceptable Cu concentration in soil porewater can be estimated by the modified TBLM through deterministic method at pH levels higher than 7, while combination of TBLM and species sensitivity distribution through the probabilistic method could be utilized at pH levels lower than 7.

• Journal of Food Hygiene and Safety
• /
• v.35 no.1
• /
• pp.51-59
• /
• 2020

This study evalutated the risk of foodborne illness from Vibrio spp. (Vibrio vulnificus and Vibrio cholerae) through sea squirt consumption. The prevalence of V. vulnificus and V. cholerae in sea squirt was evaluated, and the predictive models to describe the kinetic behavior of the Vibrio in sea squirt were developed. Distribution temperatures and times were collected, and they were fitted to probabilistic distributions to determine the appropriate distributions. The raw data from the Korea National Health and Nutrition Examination Survey 2016 were used to estimate the consumption rates and amount of sea squirt. In the hazard characterization, the Beta-Poisson model for V. vulnificus and V. cholerae infection was used. With the collected data, a simulation model was prepared and it was run with @RISK to estimate probabilities of foodborne illness by pathogenic Vibrio spp. through sea squirt consumption. Among 101 sea squirt samples, there were no V. vulnificus positive samples, but V. cholerae was detected in one sample. The developed predictive models described the fates of Vibrio spp. in sea squirt during distribution and storage, appropriately shown as 0.815-0.907 of R² and 0.28 of RMSE. The consumption rate of sea squirt was 0.26%, and the daily consumption amount was 68.84 g per person. The Beta-Poisson model [P=1-(1+Dose/β)^-α] was selected as a dose-response model. With these data, a simulation model was developed, and the risks of V. vulnificus and V. cholerae foodborne illness from sea squirt consumption were 2.66×10^-15, and 1.02×10^-12, respectively. These results suggest that the risk of pathogenic Vibrio spp. in sea squirt could be considered low in Korea.

• KIISE Transactions on Computing Practices
• /
• v.22 no.3
• /
• pp.127-132
• /
• 2016

Several methods have been proposed to apply artificial neural networks to text compression in the past. However, the networks and targets are both limited to the small size due to hardware capability in the past. Modern GPUs have much better calculation capability than CPUs in an order of magnitude now, even though CPUs have become faster. It becomes possible now to train greater and complex neural networks in a shorter time. This paper proposed a method to transform the distribution of original data with a probabilistic neural predictor. Experiments were performed on a feedforward neural network and a recurrent neural network with gated-recurrent units. The recurrent neural network model outperformed feedforward network in compression rate and prediction accuracy.

• Earthquakes and Structures
• /
• v.7 no.4
• /
• pp.485-510
• /
• 2014

In performance-based seismic design procedures Peak Ground Acceleration (PGA) and pseudo-Spectral acceleration ($S_a$) are commonly used to predict the response of structures to earthquake. Recently, research has been carried out to evaluate the predictive capability of these standard Intensity Measures (IMs) with respect to different types of structures and Engineering Demand Parameter (EDP) commonly used to measure damage. Efforts have been also spent to propose alternative IMs that are able to improve the results of the response predictions. However, most of these IMs are not usually employed in probabilistic seismic demand analyses because of the lack of reliable Ground Motion Prediction Equations (GMPEs). In order to define seismic hazard and thus to calculate demand hazard curves it is essential, in fact, to establish a GMPE for the earthquake intensity. In the light of this need, new GMPEs are proposed here for the elastic input energy spectra, energy-based intensity measures that have been shown to be good predictors of both structural and non-structural damage for many types of structures. The proposed GMPEs are developed using mixed-effects models by empirical regressions on a large number of strong-motions selected from the NGA database. Parametric analyses are carried out to show the effect of some properties variation, such as fault mechanism, type of soil, earthquake magnitude and distance, on the considered IMs. Results of comparisons between the proposed GMPEs and other from the literature are finally shown.

• The Journal of Engineering Geology
• /
• v.32 no.1
• /
• pp.27-40
• /
• 2022

Every year landslides cause serious casualties and property damages around the world. As the accurate prediction of landslides is important to reduce the fatalities and economic losses, various approaches have been developed to predict them. Prediction methods can be divided into landslide susceptibility analysis, landslide hazard analysis and landslide risk analysis according to the type of the conditioning factors, the predicted level of the landslide dangers, and whether the expected consequence cased by landslides were considered. Landslide susceptibility analyses are mainly based on the available landslide data and consequently, they predict the likelihood of landslide occurrence by considering factors that can induce landslides and analyzing the spatial distribution of these factors. Various qualitative and quantitative analysis techniques have been applied to landslide susceptibility analysis. Recently, quantitative susceptibility analyses have predominantly employed the physically based model due to high predictive capacity. This is because the physically based approaches use physical slope model to analyze slope stability regardless of prior landslide occurrence. This approach can also reproduce the physical processes governing landslide occurrence. This review examines physically based landslide susceptibility analysis approaches.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.1
• /
• pp.99-118
• /
• 2012

The aim of this study is to define a correlation between spatial distribution characteristics of FFG(Functional Feeding Groups) of aquatic insects and related environmental factors in the Geum River based on the theory of RCC(River Continuum Concept). For that objective we had used SMRA(Stepwise Multiple Regression Analysis) method to analyze close relationship between the distribution of aquatic insects and the physical and chemical factors that may affect their inhabiting environment in the study area. And then, a probabilistic method named Frequency Ratio Model(FRM) and spatial analysis function of GIS were applied to produce a predictive distribution map of biota community considering their distribution characteristics according to the environmental factors as related variables. As a result of SMRA, the values of decision coefficient for factors of elevation, stream width, flow velocity, conductivity, temperature and percentage of sand showed higher than 0.5. Therefore these 6 environmental factors were considered as major factors that might affect the distribution characteristics of aquatic insects. Finally, we had calculated RMSE(Root Mean Square Error) between the predicted distribution map and prior survey database from other researches to verify the result of this study. The values of RMSE were calculated from 0.1892 to 0.4242 according to each FFG so we could find out a high reliability of this study. The results of this study might be used to develop a new estimation method for aquatic ecosystem with macro invertebrate community and also be used as preliminary data for conservation and restoration of stream habitats.