• Title/Summary/Keyword: Prediction of variables

Search Result 1,887, Processing Time 0.03 seconds

Analysis and Validation of Geo-environmental Susceptibility for Landslide Occurrences Using Frequency Ratio and Evidential Belief Function - A Case for Landslides in Chuncheon in 2013 - (Frequency Ratio와 Evidential Belief Function을 활용한 산사태 유발에 대한 환경지리적 민감성 분석과 검증 - 2013년 춘천 산사태를 중심으로 -)

  • Lee, Won Young;Sung, Hyo Hyun;Ahn, Sejin;Park, Seon Ki
    • Journal of The Geomorphological Association of Korea
    • /
    • v.27 no.1
    • /
    • pp.61-89
    • /
    • 2020
  • The objective of this study is to characterize landslide susceptibility depending on various geo-environmental variables as well as to compare the Frequency Ratio (FR) and Evidential Belief Function (EBF) methods for landslide susceptibility analysis of rainfall-induced landslides. In 2013, a total of 259 landslides occurred in Chuncheon, Gangwon Province, South Korea, due to heavy rainfall events with a total cumulative rainfall of 296~721mm in 106~231 hours duration. Landslides data were mapped with better accuracy using the geographic information system (ArcGIS 10.6 version) based on the historic landslide records in Chuncheon from the National Disaster Management System (NDMS), the 2013 landslide investigation report, orthographic images, and aerial photographs. Then the landslides were randomly split into a testing dataset (70%; 181 landslides) and validation dataset (30%; 78 landslides). First, geo-environmental variables were analyzed by using FR and EBF functions for the full data. The most significant factors related to landslides were altitude (100~200m), slope (15~25°), concave plan curvature, high SPI, young timber age, loose timber density, small timber diameter, artificial forests, coniferous forests, soil depth (50~100cm), very well-drained area, sandy loam soil and so on. Second, the landslide susceptibility index was calculated by using selected geo-environmental variables. The model fit and prediction performance were evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under Curve (AUC) methods. The AUC values of both model fit and prediction performance were 80.5% and 76.3% for FR and 76.6% and 74.9% for EBF respectively. However, the landslide susceptibility index, with classes of 'very high' and 'high', was detected by 73.1% of landslides in the EBF model rather than the FR model (66.7%). Therefore, the EBF can be a promising method for spatial prediction of landslide occurrence, while the FR is still a powerful method for the landslide susceptibility mapping.

Estimation of Diameter and Height Growth Equations Using Environmental Variables (환경인자를 이용한 직경 및 수고생장 모형 추정)

  • Lee, Sang-Hyun
    • Journal of Korean Society of Forest Science
    • /
    • v.98 no.3
    • /
    • pp.351-356
    • /
    • 2009
  • This study purposed to judge potential possibility of building highly precise empirical model using environmental variables. Environmental variables such as altitude, mean annual rainfall, mean annual temperature and organic matter ratio of soil were added to height and diameter model for Chamaecyparis obtusa, and examined accuracy and residuals of prediction model. Improvement in precision was found for the Gompertz polymorphic height model by including mean temperature and altitude as independent variables, while the Gompertz diameter model with annual rainfall and altitude was showed improvement of precision and accuracy. Comparing the improvement of precision between the model before adding environmental variables and the model after adding them, an improvement or some ratio was obtained though it is not obvious. Therefore, there is enough proof that adding environmental variables, which can be easily acquired relatively when considering the difficulties of measurement and budget, into the model as independent variables would improve the accuracy and precision of growth models.

Fuzzy methodology application for modeling uncertainties in chloride ingress models of RC building structure

  • Do, Jeongyun;Song, Hun;So, Seungyoung;Soh, Yangseob
    • Computers and Concrete
    • /
    • v.2 no.4
    • /
    • pp.325-343
    • /
    • 2005
  • Chloride ingress is a common cause of deterioration of reinforced concrete located in coastal zone. Modeling the chloride ingress is an important basis for designing reinforced concrete structures and for assessing the reliability of an existing structure. The modeling is also needed for predicting the deterioration of a reinforced structure. The existing deterministic solution for prediction model of corrosion initiation cannot reflect uncertainties which input variables have. This paper presents an approach to the fuzzy arithmetic based modeling of the chloride-induced corrosion of reinforcement in concrete structures that takes into account the uncertainties in the physical models of chloride penetration into concrete and corrosion of steel reinforcement, as well as the uncertainties in the governing parameters, including concrete diffusivity, concrete cover depth, surface chloride concentration and critical chloride level for corrosion initiation. There are a lot of prediction model for predicting the time of reinforcement corrosion of structures exposed to chloride-induced corrosion environment. In this work, RILEM model formula and Crank's solution of Fick's second law of diffusion is used. The parameters of the models are regarded as fuzzy numbers with proper membership function adapted to statistical data of the governing parameters instead of random variables of probabilistic modeling of Monte Carlo Simulation and the fuzziness of the time to corrosion initiation is determined by the fuzzy arithmetic of interval arithmetic and extension principle. An analysis is implemented by comparing deterministic calculation with fuzzy arithmetic for above two prediction models.

Development of Prediction Model for 1-year Mortality after Hip Fracture Surgery

  • Konstantinos Alexiou;Antonios A. Koutalos;Sokratis Varitimidis;Theofilos Karachalios;Konstantinos N. Malizos
    • Hip & pelvis
    • /
    • v.36 no.2
    • /
    • pp.135-143
    • /
    • 2024
  • Purpose: Hip fractures are associated with increased mortality. The identification of risk factors of mortality could improve patient care. The aim of the study was to identify risk factors of mortality after surgery for a hip fracture and construct a mortality model. Materials and Methods: A cohort study was conducted on patients with hip fractures at two institutions. Five hundred and ninety-seven patients with hip fractures that were treated in the tertiary hospital, and another 147 patients that were treated in a secondary hospital. The perioperative data were collected from medical charts and interviews. Functional Assessment Measure score, Short Form-12 and mortality were recorded at 12 months. Patients and surgery variables that were associated with increased mortality were used to develop a mortality model. Results: Mortality for the whole cohort was 19.4% at one year. From the variables tested only age >80 years, American Society of Anesthesiologists category, time to surgery (>48 hours), Charlson comorbidity index, sex, use of anti-coagulants, and body mass index <25 kg/m2 were associated with increased mortality and used to construct the mortality model. The area under the curve for the prediction model was 0.814. Functional outcome at one year was similar to preoperative status, even though their level of physical function dropped after the hip surgery and slowly recovered. Conclusion: The mortality prediction model that was developed in this study calculates the risk of death at one year for patients with hip fractures, is simple, and could detect high risk patients that need special management.

Strain demand prediction of buried steel pipeline at strike-slip fault crossings: A surrogate model approach

  • Xie, Junyao;Zhang, Lu;Zheng, Qian;Liu, Xiaoben;Dubljevic, Stevan;Zhang, Hong
    • Earthquakes and Structures
    • /
    • v.20 no.1
    • /
    • pp.109-122
    • /
    • 2021
  • Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensional nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

Prediction factors for dating sexual violence of College Students (대학생의 데이트 성폭력 가해 예측요인)

  • Lee, Mee-Ho
    • The Journal of Korean Society for School & Community Health Education
    • /
    • v.21 no.3
    • /
    • pp.35-47
    • /
    • 2020
  • Objectives: This study is a descriptive research study conducted to grasp the Prediction factors of the sexual violence experience of college students. Methods: A convenience sampling was performed for 500 students from one college located in Gyeongsangbuk-do, who agreed to the purpose of this study. Data collection was conducted from October 5, 2015, to October 23, 2015, by filling out the self-report questionnaire. Among the 450 subjects excluding those with missing values, a questionnaire of dating violence experience was applied to 317 college students who answered that they had a friend of the opposite sex, and variables and prediction factors related to dating violence experiences were identified. The statistical methods used were descriptive statistics, x2-test, t-test, Pearson's correlation coefficient and binary logistic regression analysis. Results: As a result of the study, the experience of sexual behavior before entering college (𝑥2=6.52, p=.011), experience of sexual violence damage before entering college(p=.045), the experience of sexual assault before entering college (p=.007) and experience of school violence damage(p=.002) were variables related to the sexual violence experience of college students. School violence victimization (OR=4.831, p=.007) and controlling dating partners (OR=1.349, p<.001) were predictors of dating sexual violence. Dating sexual violence experience group were compared to dating sexual violence non-experience group, the relative degree of controlling dating partners was high (t=4.25, p<.001) and had a traditional gender role attitude (t=2.94, p=.004). and there was a positive correlation (r=.358, p<.001) between controlling dating partners and gender role attitude. Conclusions: In order to prevent sexual violence on dating among college students, it is expected that more effective health education results will emerge if the contents of the school-age school violence victimization experience and the control of dating partners, which are predicted factors of sexual violence on dating, are included in the sexual violence prevention program.

Non-invasive hematocrit measurement (혈액중 non-invasive hematocrit 분석)

  • Yoon, Gil-Won;Jeon, Kye-Jin;Park, Kun-Kook;Lee, Jong-Youn;Hwang, Hyun-Tae;Yeo, Hyung-Seok;Kim, Hong-Sig
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
    • /
    • 2002.11a
    • /
    • pp.59-62
    • /
    • 2002
  • Wavelength selection and prediction algorithm for determining hematocrit are investigated. A model based on the difference in optical density induced by the pulsation of heart beat is developed by taking approximation of Twersky's theory on the assumption that the variation of blood vessel size is small during arterial pulsing[1]. A device is constructed with a five-wavelength LED array as light source. The selected wavelengths are two isobestic points and three in compensation for tissue scattering. Data are collected from 549 out-patients who are randomly grouped as calibration and prediction sets. The range of percent hematocrit was 19.3∼51.8. The ratio of the variations of optical density between systole and diastole at two different wavelengths is used as a variable. We selected several such variables that show high reproducibility among all variables. Multiple linear regression analysis is made. The relative percent error is 8% and the standard deviation is 3.67 for the calibration set. The relative % error and standard deviation of the prediction set are 8.2% and 3.69 respectively. We successfully demonstrate the possibility of non-invasive hematocrit measurement, particularly, using the wavelengths below 1000nm.

  • PDF

Evaluation of the equation for predicting dry matter intake of lactating dairy cows in the Korean feeding standards for dairy cattle

  • Lee, Mingyung;Lee, Junsung;Jeon, Seoyoung;Park, Seong-Min;Ki, Kwang-Seok;Seo, Seongwon
    • Animal Bioscience
    • /
    • v.34 no.10
    • /
    • pp.1623-1631
    • /
    • 2021
  • Objective: This study aimed to validate and evaluate the dry matter (DM) intake prediction model of the Korean feeding standards for dairy cattle (KFSD). Methods: The KFSD DM intake (DMI) model was developed using a database containing the data from the Journal of Dairy Science from 2006 to 2011 (1,065 observations 287 studies). The development (458 observations from 103 studies) and evaluation databases (168 observations from 74 studies) were constructed from the database. The body weight (kg; BW), metabolic BW (BW0.75, MBW), 4% fat-corrected milk (FCM), forage as a percentage of dietary DM, and the dietary content of nutrients (% DM) were chosen as possible explanatory variables. A random coefficient model with the study as a random variable and a linear model without the random effect was used to select model variables and estimate parameters, respectively, during the model development. The best-fit equation was compared to published equations, and sensitivity analysis of the prediction equation was conducted. The KFSD model was also evaluated using in vivo feeding trial data. Results: The KFSD DMI equation is 4.103 (±2.994)+0.112 (±0.022)×MBW+0.284 (±0.020)×FCM-0.119 (±0.028)×neutral detergent fiber (NDF), explaining 47% of the variation in the evaluation dataset with no mean nor slope bias (p>0.05). The root mean square prediction error was 2.70 kg/d, best among the tested equations. The sensitivity analysis showed that the model is the most sensitive to FCM, followed by MBW and NDF. With the in vivo data, the KFSD equation showed slightly higher precision (R2 = 0.39) than the NRC equation (R2 = 0.37), with a mean bias of 1.19 kg and no slope bias (p>0.05). Conclusion: The KFSD DMI model is suitable for predicting the DMI of lactating dairy cows in practical situations in Korea.

Using Artificial Neural Networks for Forecasting Algae Counts in a Surface Water System

  • Coppola, Emery A. Jr.;Jacinto, Adorable B.;Atherholt, Tom;Poulton, Mary;Pasquarello, Linda;Szidarvoszky, Ferenc;Lohbauer, Scott
    • Korean Journal of Ecology and Environment
    • /
    • v.46 no.1
    • /
    • pp.1-9
    • /
    • 2013
  • Algal blooms in potable water supplies are becoming an increasingly prevalent and serious water quality problem around the world. In addition to precipitating taste and odor problems, blooms damage the environment, and some classes like cyanobacteria (blue-green algae) release toxins that can threaten human health, even causing death. There is a recognized need in the water industry for models that can accurately forecast in real-time algal bloom events for planning and mitigation purposes. In this study, using data for an interconnected system of rivers and reservoirs operated by a New Jersey water utility, various ANN models, including both discrete prediction and classification models, were developed and tested for forecasting counts of three different algal classes for one-week and two-weeks ahead periods. Predictor model inputs included physical, meteorological, chemical, and biological variables, and two different temporal schemes for processing inputs relative to the prediction event were used. Despite relatively limited historical data, the discrete prediction ANN models generally performed well during validation, achieving relatively high correlation coefficients, and often predicting the formation and dissipation of high algae count periods. The ANN classification models also performed well, with average classification percentages averaging 94 percent accuracy. Despite relatively limited data events, this study demonstrates that with adequate data collection, both in terms of the number of historical events and availability of important predictor variables, ANNs can provide accurate real-time forecasts of algal population counts, as well as foster increased understanding of important cause and effect relationships, which can be used to both improve monitoring programs and forecasting efforts.

The Comparison of Peach Price and Trading Volume Prediction Model Using Machine Learning Technique (기계학습을 이용한 복숭아 경락가격 및 거래량 예측모형 비교)

  • Kim, Mihye;Hong, Sungmin;Yoon, Sanghoo
    • Journal of the Korean Data Analysis Society
    • /
    • v.20 no.6
    • /
    • pp.2933-2940
    • /
    • 2018
  • It is known that fruit is more affected by the weather than other crops. Therefore, in order to create high value for farmers, it is necessary to develop a wholesale price model considering the weather. Peaches produced under relatively limited conditions were chosen as subjects of study. The data were collected from 2015 to 2017 provided by okdab 4.0. The meteorological data used for the analysis were generated by weighting the cultivation area and the variables with high correlation among the weather data were selected from the day before to 7 days before. Randomforest, gradient boosting machine, and XGboost were used for the analysis. As a result of analysis, XGboost showed the best performance in the sense of RMSE and correlation, and price prediction was comparatively well predicted, but the accuracy of the trading volume prediction was not so good enough. The top three weather variables affecting to the peach were minimum temperature, average maximum temperature, and precipitation.