• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.109-119
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• 2016

The height datum of Korea is currently separated into land and sea, which makes it difficult to acquire homogeneous and accurate height information throughout the whole nation. In this study, we therefore tried to suggest the more effective way to transform the height information were constructed separately according to each height datum on land and sea to those on the unique height datum using precise geoid models and tidal observations in Korea. For this, Anmyeon island was selected as a study area to develop the precise geoid models based on the height datums land (IMSL) and sea (LMSL), respectively. In order to develop two hybrid geoid models based on each height datum of land an sea, we firstly develop a precise gravimetric geoid model using the remove and restore (R-R) technique with all available gravity observations. The gravimetric geoid model were then fitted to the geometric geoidal heights, each of which is represented as height datum of land or sea respectively, obtained from GPS/Leveling results on 15 TBMs in the study area. Finally, we determined the differences between the two hybrid geoid models to apply the height transformation between IMSL and LMSL. The co-tidal chart model of TideBed system developed by Korea Hydrographic and Oceanographic Agency (KHOA) which was re-gridded to have the same grid size and coverage as the geoid model, in order that this can be used for the height datum transformation from LMSL to local AHHW and/or from LMSL to local DL. The accuracy of height datum transformation based on the strategy suggested in this study was approximately ${\pm}3cm$. It is expected that the results of this study can help minimize not only the confusions on the use of geo-spatial information due to the disagreement caused by different height datum, land and sea, in Korea, but also the economic and time losses in the execution of coastal development and disaster prevention projects in the future.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Korean Society of Steel Construction
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• v.10 no.3 s.36
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• pp.553-564
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• 1998

Recently, more and more steel-deck structural system for two story roads has been adopted as a solution against traffic congestion in urban area, mainly because of fast construction, reduced self-weight, higher stiffness and efficient erection compared to that of concrete decks. The main objective is to study on the unit-elective optimal type and proportioning of a rational steel-deck system for two story roads using an optimum design program specifically developed for steel-deck systems. The objective function for the optimization is formulated as a minimum cost design problem. The behavior and design constraints are formulated based on the ASD(Allowable Stress Design) criteria of the Korean Bridge Design Code. The optimum design program developed in this study consists of two steps - the first step for the optimization of the steel box or plate girder viaducts, and the second step for the optimum design of the steel-decks with closed or open ribs. A grid model is used as a structural analysis model for the optimization of the main girder system, while the analysis of the deck system is based on the Pelican-Esslinger method. The SQP(Sequential Quadratic Programming) is used as the optimization technique for the constrained optimization problem. By using a set of application examples, the rational type related to the optimized steel-deck system designs is investigated by comparing the cost effectiveness of each type. Based on the results of the investigation it may be concluded that the optimal linear box girder and deck system with closed ribs may be utilized as one of the most rational and economical viaducts in the construction of two-story roads.

• Geophysics and Geophysical Exploration
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• v.11 no.4
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• pp.318-325
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• 2008

Assigning an exact boundary condition is of great importance in three-dimensional (3D) magnetotelluric (MT) modeling, in which no source is considered in a computing domain. This paper presents a 3D MT modeling algorithm utilizing a Dirichlet condition for a 2D host. To compute boundary values for a model with a 2D host, we need to conduct additional 2D MT modeling. The 2D modeling consists of transverse magnetic and electric modes, which are determined from the relationship between the polarization of plane wave and the strike direction of the 2D structure. Since the 3D MT modeling algorithm solves Maxwell's equations for electric fields using the finite difference method with a staggered grid that defines electric fields along cell edges, electric fields are calculated at the same place in the 2D modeling. The algorithm developed in this study can produce reliable MT responses for a 3D model with a 2D host.

• Journal of the Korean association of regional geographers
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• v.9 no.2
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• pp.203-216
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• 2003

The process of constructing coastal digital elevation model(DEM), for the 3 dimensional analysis, is composed by abstracting land layers for land elevation and water depth, reprojecting UTM, relocating geographical grid, and interpolating works. The geomorphic set of shallow sea, including tidal current, tidal zone deposition, and water depth distribution, was analyzed by eye search of Landsat TM image, masking of land zone, band combination and regression analysis. Some horizontal differences, between combined DEM and surveyed data of shallow sea, was corrected for analysis. Analyzed geomorphic elements are stream channel, alluvial fan, coastal terrace, tidal current. and shallow sea bank. Results of analysis present that transported fluvial materials influence tidal sedimentation, especially from Gahwacheon river, for the role of artificial draining flooding waters from Jinyang Reservoir, almost in the summer season. In the coastal area with less tidal current, more fine materials are deposited. The influence of currental deposition are higher on small pockets with west coast of well developed terraces. The lower skirt of alluvial fans developed into the tidal zone of shallow sea. Small pocket type bays are closed by coastal current, and less influenced from tidal deposition. The bank of Jinju Bay are developed originally from submerging of remnant erosional mountain ranges, and play on the role of trapping fine materials.

• Journal of the Korean earth science society
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• v.32 no.7
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• pp.719-731
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• 2011

Based on the ICE-3G and ICE-5G ice models, we predicted the velocities of crustal uplift caused by Glacial Isostatic Adjustment (GIA) at 39 tide gauge sites operated by Korea Hydrographic and Oceanographic Administration (KHOA). We also divided the Korean peninsula in the ranges of $32-38.5^{\circ}N$ and $124-132^{\circ}E$ in $0.5^{\circ}{\times}0.5^{\circ}$ grids, and computed the GIA velocities at each grid point. We found that the average uplift rates due to GIA in South Korea were 0.33 and 1.21 mm/yr for ICE-3G and ICE-5G, respectively. Because the GIA rates were relatively high at ~1 mm/yr when the updated ice model ICE-5G was used, we concluded that the GIA effect cannot be neglected when we compute the absolute sea level (ASL) rates around the Korean peninsula. In this study, we corrected the ICE-5G GIA velocities from the relative sea level rates provided by KHOA and we computed the ASL rates at 13 tide gauge stations. As a result, we found that the average ASL velocity around the Korean peninsula was 5.04 mm/yr. However, the ASL rates near Jeju island were abnormally higher than the other areas and the average was 8.84 mm/yr.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.4
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• pp.61-71
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• 2001

In these days, surveying instruments are developing rapidly and the precision is improving continuously. The reappearance of three dimensional terrains of a great precision are possible and the calculation of the area or the volume has a high precision due to the development of the technique of the spatial information system using computer. But actually, in construction site they calculate two-dimensional area using the traditional method, plane table surveying, planimeter, and then get ground surface area through timing the slope correction factor. In this study, I show the defect and inefficiency of the calculation of the area by the traditional methods and survey the area with Electronic Distance Measuring equipment and GPS instrument. With these data, we made the three dimensional terrain model and calculated two-dimensional area and ground surface area. After that, I compared areas that calculated by algorithm method of irregular triangle and analysis of grid method with standardizing the area that calculated by the traditional method. Finally, I suggested more effective and precise method in calculating ground surface area.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.40-48
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• 2011

The purpose of this study is to contribute enhancing the accuracy of viewshed analysis through the explanation for an analysis method of viewshed analysis using GIS. According to previous studies, the visible area using digital terrain in viewshed analysis depends on a visible interest area, scale of terrain, spatial resolution and surface data. In this study, we used trend analysis and RMSE analysis in order to find the effect of a visible interest area, scale of terrain, etc in viewshed analysis. Results of this study are as follows. First, the result of viewshed analysis depends on a visible interest area, scale of terrain, spatial resolution, surface data such as previous studies. Second, the results in forest area are reliable than those of flat area in terms of a visible interest area. Third, the results based on raster grid data are stable than those of TIN(triangulated irregular network) in terms of input surface data. Fourth, according to the result of trend and RMSE analysis, the spatial resolution for analysis is differently applied to different scales digital terrain map in viewshed analysis. In detail, it is desirable that the spatial resolution is set less than 10m(in the case of 1/1,000 digital terrain map), 20m(in the case of 1/5,000 map), 30m(1/25,000 map).

• Journal of Ocean Engineering and Technology
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• v.33 no.6
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• pp.590-596
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• 2019

Numerous deaths and substantial property damage have occurred recently due to frequent disasters of the highest intensity according to the abnormal climate, which is caused by various problems, such as global warming, all over the world. Such large-scale disasters have become an international issue and have made people aware of the disasters so they can implement disaster-prevention measures. Extensive information on disaster prevention actively has been announced publicly to support the natural disaster reduction measures throughout the world. In Japan, diverse developmental studies on disaster prevention systems, which support hazard map development and flood control activity, have been conducted vigorously to estimate external forces according to design frequencies as well as expected maximum frequencies from a variety of areas, such as rivers, coasts, and ports based on broad disaster prevention data obtained from several huge disasters. However, the current reduction measures alone are not sufficiently effective due to the change of the paradigms of the current disasters. Therefore, in order to obtain the synergy effect of reduction measures, a study of the establishment of an integrated system is required to improve the various disaster prevention technologies and the current disaster prevention system. In order to develop a similar typhoon search system and establish a disaster prevention infrastructure, in this study, techniques will be developed that can be used to forecast typhoons before they strike by using artificial intelligence (AI) technology and offer primary disaster prevention information according to the direction of the typhoon. The main function of this model is to predict the most similar typhoon among the existing typhoons by utilizing the major typhoon information, such as course, central pressure, and speed, before the typhoon directly impacts South Korea. This model is equipped with a combination of AI and DNN forecasts of typhoons that change from moment to moment in order to efficiently forecast a current typhoon based on similar typhoons in the past. Thus, the result of a similar typhoon search showed that the quality of prediction was higher with the grid size of one degree rather than two degrees in latitude and longitude.