• Title, Summary, Keyword: Occurrence Prediction

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Development of technique for slope hazards prediction using decision tree model (의사결정나무모형을 이용한 급경사지재해 예측기법 개발)

  • Song, Young-Suk;Cho, Yong-Chan;Chae, Byung-Gon
    • Proceedings of the Korean Geotechical Society Conference
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    • pp.233-242
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    • 2009
  • Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

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Development and Comparison of Data Mining-based Prediction Models of Building Fire Probability

  • Hong, Sung-gwan;Jeong, Seung Ryul
    • Journal of Internet Computing and Services
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    • v.19 no.6
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    • pp.101-112
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    • 2018
  • A lot of manpower and budgets are being used to prevent fires, and only a small portion of the data generated during this process is used for disaster prevention activities. This study develops a prediction model of fire occurrence probability based on data mining in order to more actively use these data for disaster prevention activities. For this purpose, variables for predicting fire occurrence probability of various buildings were selected and data of construction administrative system, national fire information system, and Korea Fire Insurance Association were collected and integrated data set was constructed. After appropriate data cleansing and preprocessing, various data mining methodologies such as artificial neural network, decision trees, SVM, and Naive Bayesian were used to develop a prediction model of the fire occurrence probability of buildings. The most accurate model among the derived models is Linear SVM model which shows 68.42% as experimental data and 63.54% as verification data and it is the best model to predict fire occurrence probability of buildings. As this study develops the prediction model which uses only the set values of the specific ranges, future studies may explore more opportunites to use various setting values not shown in this study.

Analysis of Landslide Hazard Probability for Cultural Heritage Site using Landslide Prediction Map (산사태예측도에 의한 석조문화재 주변의 산사태재해 가능성 분석)

  • Kim, Kyeong-Su;Lee, Choon-Oh;Song, Yeung-Suk;Cho, Yong-Chan;Kim, Man-Il;Chae, Byung-Gon
    • The Journal of Engineering Geology
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    • v.17 no.3
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    • pp.411-418
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    • 2007
  • It is a very difficult thing to estimate an occurrence possibility location and hazard expectation area by landslide. The prediction difficulty of landslide occurrence has relativity in factor of various geological physical factors and contributions. However, estimation of landslide occurrence possibility and classification of hazard area became available correlation mechanism through analysis of landslide occurrence through landslide data analysis and statistical analysis. This study analyzed a damage possibility of a cultual heritage area due to landslide occurrence by a heavy rainfall. We make a landslide prediction map and tried to analysis of landslide occurrence possibility for the cultural heritage site. The study area chooses a temple of Silsang-Sa Baekjang-Am site and made a landslide prediction map. In landslide prediction map, landslide hazard possibility area expressed by occurrence probability and divided by each of probability degrees. This degree used to evaluate occurrence possibility for existence and nonexistence of landslide in the study site. For the prediction and evaluation of landslide hazard for the cultural heritage site, investigation and analysis technique which is introduced in this study may contribute an efficient management and investigation in the cultural heritage site, Korea.

Prediction of Landslide around Stone Relics of Jinjeon-saji Area (진전사지 석조문화재 주변의 산사태예측)

  • Kim, Kyeong-Su;Lee, Choon-Oh;Song, Young-Suk;Cho, Yong-Chan
    • Proceedings of the Korean Geotechical Society Conference
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    • pp.1378-1385
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    • 2008
  • The probability of landslide hazards was predicted to natural terrain around the stone relics of Jinjeon-saji area, which is located in Yangyang, Kangwon Province. As the analysis results of field investigation, laboratory test and geology and geomorphology data, the effect factors of landslides occurrence were evaluated, and then the landslides prediction map was made up by use of prediction model considering the effect factors. The susceptibility of stone relics induced by landslides was investigated as the grading classification of occurrence probability using the landslides prediction map. In the landslides prediction map, the high probability area of landslides over 70% of occurrence probability was 3,489m3, which was 10.1% of total prediction area. If landslides are occurred at the high elevation area, the three stories stone pagoda of Jinjeon-saji (National treasure No.122) and the stone lantern of Jinjeon-saji (Treasure No.439) will be collapsed by debris flow.

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Red Tide Prediction in the Korean Coastal Areas by RS and GIS

  • Yoon, Hong-Joo
    • Proceedings of the KSRS Conference
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    • v.1
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    • pp.332-335
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    • 2006
  • Red tide(harmful algae) in the Korean Coastal Waters has a given a great damage to the fishery every year. However, the aim of our study understands the influence of meteorological factors (air and water temperature, precipitation, sunshine, solar radiation, winds) relating to the mechanism of red tide occurrence and monitors red tide by satellite remote sensing, and analyzes the potential area for red tide occurrence by GIS. The meteorological factors have directly influenced on red tide formation. Thus, We want to predict and apply to red tide formation from statistical analyses on the relationships between red tide formation and meteorological factors. In future, it should be realized the near real time monitoring for red tide by the development of remote sensing technique and the construction of integrated model by the red tide information management system (the data base of red tide - meteorological informations). Finally our purpose is support to the prediction information for the possible red tide occurrence by coastal meteorological information and contribute to reduce the red tide disaster by the prediction technique for red tide.

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Cancer Prediction Based on Radical Basis Function Neural Network with Particle Swarm Optimization

  • Yan, Xiao-Bo;Xiong, Wei-Qing;Hu, Liang;Zhao, Kuo
    • Asian Pacific Journal of Cancer Prevention
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    • v.15 no.18
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    • pp.7775-7780
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    • 2014
  • This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.

Life Risk Assessment of Landslide Disaster Using Spatial Prediction Model (공간 예측 모델을 이용한 산사태 재해의 인명 위험평가)

  • Jang, Dong-Ho;Chung, C.F.
    • Journal of Environmental Impact Assessment
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    • v.15 no.6
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    • pp.373-383
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    • 2006
  • The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

Development and its APPLIcation of Computer Program for Slope Hazards Prediction using Decision Tree Model (의사결정나무모형을 이용한 급경사지재해 예측프로그램 개발 및 적용)

  • Song, Young-Suk;Cho, Yong-Chan;Seo, Yong-Seok;Ahn, Sang-Ro
    • Journal of The Korean Society of Civil Engineers
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    • v.29 no.2C
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    • pp.59-69
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    • 2009
  • Based on the data obtained from field investigation and soil testing to slope hazards occurrence section and non-occurrence section in crystalline rocks like gneiss, granite, and so on, a prediction model was developed by the use of a decision tree model. The classification standard of the selected prediction model is composed of the slope angle, the coefficient of permeability and the void ratio in the order. The computer program, SHAPP ver. 1.0 for prediction of slope hazards around an important national facilities using GIS technique and the developed model. To prove the developed prediction model and the computer program, the field data surveyed from Jumunjin, Gangneung city were compared with the prediction result in the same site. As the result of comparison, the real occurrence location of slope hazards was similar to the predicted section. Through the continuous study, the accuracy about prediction result of slope hazards will be upgraded and the computer program will be commonly used in practical.

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The Evaluation on the Prediction Ratio of Landslide Hazard Area based on Geospatial Information (공간정보 기반 산사태 발생지역 예측비율 평가)

  • Lee, Geun-Sang;Lee, Ho-Jun;Go, Sin-Young;Cho, Gi-Sung
    • Journal of Cadastre & Land InformatiX
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    • v.44 no.2
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    • pp.113-124
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    • 2014
  • Recently landslide occurs frequently by heavy rainfall, therefore there area many studies to analyze the vulnerable district of landslide and forecast the occurrence of landslide. This study analyzed soil characteristics in the occurrence district of landslide and the occurrence possibility of landslide ranked high in well draining soil as the result of frequency ratio according to the characteristics of drainage. Also as the result of frequency ratio of slope derived from DEM data, the occurrence possibility of landslide ranked high in slope range of $20{\sim}40^{\circ}$. And Also as the result of frequency ratio of aspect by geospatial analysis, the occurrence possibility of landslide ranked high in north aspect. Also, it is possible to evaluate the vulnerability of landslide by overlapping frequency ratio of the drainage of soil, slope and aspect. And future prediction ratio of landslide occurrence can be evaluated by performing the analysis and validation process respectively on the subject of the occurrence district of landslide.