• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.3
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• pp.795-804
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• 2014

In this study, Andong Reservoir monthly and ten days inflows from July 2011 to September 2011 are predicted using SWAT model and ensemble technique. The weight method using monthly and ten days rainfall forecasts from Korea Meteorological Administration is applied for accurate analysis. If the rainfall prediction announced by Korea Meteorological Administration is close to the actual rainfall, the PDF-Ratio Method shows the best result. If the past high rainfall occurrence is close to the actual rainfall, the modified PDF-Ratio method shows the best result. This method can improve the prediction accuracy even though the Korea Meteorological Administration forecast is not accurate. On the contrary, if Korea Meteorological Administration forecast is different from the actual rainfall and the past rainfall occurrence statistics of lower section, the uniform method shows the best result.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.21 no.1
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• pp.103-118
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• 2018

Ecosystems in subalpine regions are recognized as areas vulnerable to climatic changes because rainfall and the possibility of flora migration are very low due to the characteristics of topography in the regions. In this context, habitat niche was formulated for representative species of arbors in subalpine regions in order to understand the effects of climatic changes on alpine arbor ecosystems. The current potential habitats were modeled as future change areas according to the climatic change scenarios. Based on the growth conditions and environmental characteristics of the habitats, the study was conducted to identify direct and indirect causes affecting the habitat reduction of Abies nephrolepis. Diverse model algorithms for explanation of the relationship between the emergence of biological species and habitat environments were reviewed to construct the environmental data suitable for the six models(GLM, GAM, RF, MaxEnt, ANN, and SVM). Weights determined through TSS were applied to the six models for ensemble in an attempt to minimize the uncertainty of the models. Based on the current climate determined by averaging the climates over the past 30years(1981~2010) and the HadGEM-RA model was applied to fabricate bioclimatic variables for scenarios RCP 4.5 and 8.5 on the near and far future. The results of models of the alpine region tree species studied were put together and evaluated and the results indicated that a total of eight national parks such as Mt. Seorak, Odaesan, and Hallasan would be mainly affected by climatic changes. Changes in the Baekdudaegan reserves were analyzed and in the results, A. nephrolepis was predicted to be affected the most in the RCP8.5. The results of analysis as such are expected to be finally utilizable in the survey of biological species in the Korean peninsula, restoration and conservation strategies considering climatic changes as the analysis identified the degrees of impacts of climatic changes on subalpine region trees in Korean peninsula with very high conservation values.

• Journal of Korean Society on Water Environment
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• v.27 no.3
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• pp.264-272
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• 2011

The objective of this study was to develop a model for predicting long-term runoff in a basin using the ensemble streamflow prediction (ESP) technique and review its reliability. To achieve the objective, this study improved not only the ESP technique based on the ensemble scenario analysis of historical rainfall data but also conventional ESP techniques used in conjunction with qualitative climate forecasting information, and analyzed and assessed their improvement effects. The model was applied to the Geum River basin. To undertake runoff forecasting, this study tried three cases (case 1: Climate Outlook + ESP, case 2: ESP probability through monthly measured discharge, case 3: Season ESP probability of case 2) according to techniques used to calculate ESP probabilities. As a result, the mean absolute error of runoff forecasts for case 1 proposed by this study was calculated as 295.8 MCM. This suggests that case 1 showed higher reliability in runoff forecasting than case 2 (324 MCM) and case 3 (473.1 MCM). In a discrepancy-ratio accuracy analysis, the Climate Outlook + ESP technique displayed 50.0%. This suggests that runoff forecasting using the Climate Outlook +ESP technique with the lowest absolute error was more reliable than other two cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.70-75
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• 2022

Accurately measuring location is necessary to provide a variety of services. The data for indoor positioning measures the RSSI values from the WiFi device through an application of a smartphone. The measured data becomes the raw data of machine learning. The feature data is the measured RSSI value, and the label is the name of the space for the measured position. For this purpose, the machine learning technique is to study a technique that predicts the exact location only with the WiFi signal by applying an efficient technique to classification. Ensemble is a technique for obtaining more accurate predictions through various models than one model, including backing and boosting. Among them, Boosting is a technique for adjusting the weight of a model through a modeling result based on sampled data, and there are various algorithms. This study uses Xgboost among the above techniques and evaluates performance with other ensemble techniques.

• Atmosphere
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• v.32 no.1
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• pp.1-15
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• 2022

Recently, Japan's Meteorological Research Institute presented the d4PDF database (Database for Policy Decision-Making for Future Climate Change, d4PDF) through large-scale climate ensemble simulations to overcome uncertainty arising from variability when the general circulation model represents extreme-scale precipitation. In this study, the change of precipitation characteristics between the historical and future climate conditions in the Yongdam-dam basin was analyzed using the d4PDF data. The result shows that annual mean precipitation and seasonal mean precipitation increased by more than 10% in future climate conditions. This study also performed an analysis on the change of the return period rainfall. The annual maximum daily rainfall was extracted for each climatic condition, and the rainfall with each return period was estimated. In this process, we represent the extreme-scale rainfall corresponding to a very long return period without any statistical model and method as the d4PDF provides rainfall data during 3,000 years for historical climate conditions and during 5,400 years for future climate conditions. The rainfall with a 50-year return period under future climate conditions exceeded the rainfall with a 100-year return period under historical climate conditions. Consequently, in future climate conditions, the magnitude of rainfall increased at the same return period and, the return period decreased at the same magnitude of rainfall. In this study, by using the d4PDF data, it was possible to analyze the change in extreme magnitude of rainfall.

• Journal of the Korean Geosynthetics Society
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• v.23 no.1
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• pp.17-25
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• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.39-46
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• 2024

Many researchers make efforts to evaluate water quality using various models. Such models require a dataset without missing values, but in real world, most datasets include missing values for various reasons. Simple deletion of samples having missing value(s) could distort distribution of the underlying data and pose a significant risk of biasing the model's inference when the missing mechanism is not MCAR. In this study, to explore the most appropriate technique for handing missing values in water quality data, several imputation techniques were experimented based on existing KNN and MICE imputation with/without the generative neural network model, Autoencoder(AE) and Denoising Autoencoder(DAE). The results shows that KNN and MICE combined imputation without generative networks provides the closest estimated values to the true values. When evaluating binary classification models based on support vector machine and ensemble algorithms after applying the combined imputation technique to the observed water quality dataset with missing values, it shows better performance in terms of Accuracy, F1 score, RoC-AuC score and MCC compared to those evaluated after deleting samples having missing values.

• Information Systems Review
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• v.21 no.4
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• pp.31-45
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• 2019

Despite the expansion of the market of performing arts and culture, small and medium size theaters are still experiencing difficulties due to poor accessibility of information by consumers. This study proposes a machine learning based genre recommendation system as an alternative to enhance the marketing capability of small and medium sized theaters. We developed five recommendation systems that recommend three genres per customer using customer master DB and transaction history DB of domestic venues. We propose an optimal recommendation system by comparing performances of recommendation system. As a result, the recommendation system based on the ensemble model showed better performance than the single predictive model. This study applied the personalized recommendation technique which was scarce in the field of performing arts and culture, and suggests that it is worthy enough to use it in the field of performing arts and culture.

• Journal of Korea Water Resources Association
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• v.44 no.5
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• pp.389-406
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• 2011

The objective of this study is to examine the climate change impact assessment on Korean water resources considering the uncertainties of Global Climate Models (GCMs) and hydrological models. The 3 different emission scenarios (A2, A1B, B1) and 13 GCMs' results are used to consider the uncertainties of the emission scenario and GCM, while PRMS, SWAT, and SLURP models are employed to consider the effects of hydrological model structures and potential evapotranspiration (PET) computation methods. The 312 ensemble results are provided to 109 mid-size sub-basins over South Korean and Gaussian kernel density functions obtained from their ensemble results are suggested with the ensemble mean and their variabilities of the results. It shows that the summer and winter runoffs are expected to be increased and spring runoff to be decreased for the future 3 periods relative to past 30-year reference period. It also provides that annual average runoff increased over all sub-basins, but the increases in the northern basins including Han River basin are greater than those in the southern basins. Due to the reason that the increase in annual average runoff is mainly caused by the increase in summer runoff and consequently the seasonal runoff variations according to climate change would be severe, the climate change impact on Korean water resources could intensify the difficulties to water resources conservation and management. On the other hand, as regards to the uncertainties, the highest and lowest ones are in winter and summer seasons, respectively.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.