• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.79-96
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• 2021

Green infrastructure planning represents landscape planning measures to reduce particulate matter. This study aimed to derive factors that may be used in planning green infrastructure for particulate matter reduction using text mining techniques. A range of analyses were carried out by focusing on keywords such as 'particulate matter reduction plan' and 'green infrastructure planning elements'. The analyses included Term Frequency-Inverse Document Frequency (TF-IDF) analysis, centrality analysis, related word analysis, and topic modeling analysis. These analyses were carried out via text mining by collecting information on previous related research, policy reports, and laws. Initially, TF-IDF analysis results were used to classify major keywords relating to particulate matter and green infrastructure into three groups: (1) environmental issues (e.g., particulate matter, environment, carbon, and atmosphere), target spaces (e.g., urban, park, and local green space), and application methods (e.g., analysis, planning, evaluation, development, ecological aspect, policy management, technology, and resilience). Second, the centrality analysis results were found to be similar to those of TF-IDF; it was confirmed that the central connectors to the major keywords were 'Green New Deal' and 'Vacant land'. The results from the analysis of related words verified that planning green infrastructure for particulate matter reduction required planning forests and ventilation corridors. Additionally, moisture must be considered for microclimate control. It was also confirmed that utilizing vacant space, establishing mixed forests, introducing particulate matter reduction technology, and understanding the system may be important for the effective planning of green infrastructure. Topic analysis was used to classify the planning elements of green infrastructure based on ecological, technological, and social functions. The planning elements of ecological function were classified into morphological (e.g., urban forest, green space, wall greening) and functional aspects (e.g., climate control, carbon storage and absorption, provision of habitats, and biodiversity for wildlife). The planning elements of technical function were classified into various themes, including the disaster prevention functions of green infrastructure, buffer effects, stormwater management, water purification, and energy reduction. The planning elements of the social function were classified into themes such as community function, improving the health of users, and scenery improvement. These results suggest that green infrastructure planning for particulate matter reduction requires approaches related to key concepts, such as resilience and sustainability. In particular, there is a need to apply green infrastructure planning elements in order to reduce exposure to particulate matter.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Korea Water Resources Association
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• v.57 no.8
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• pp.533-548
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• 2024

Due to climate change with extreme weather events, occurrences of unprecedented heavy rainfall have become more frequent. Since it is difficult to perfectly predict and prevent flood damages, the limitation of traditional prevention-centered approaches has come a issue. The concept of "resilience" has therefore been developed which emphasizes the ability to swiftly recover from damages to previous states or to even better conditions. In this study, we 1) developed a total of 20 domestic flood resilience indicators based on the 4R principles (Redundancy, Robustness, Rapidity, Resourcefulness), 2) conducted applicability evaluations targeting specific disaster-prone areas, and 3) assessed the importance of each indicator through Analytic Hierarchy Process (AHP) analysis with flood-related experts. To confirm the suitability of the developed flood resilience indicators, multicollinearity analysis was performed, and the results indicated that all 20 indicators had tolerance limits above 0.1 and Variance Inflation Factors (VIF) below 10, demonstrating suitability as factors. Furthermore, evaluations of proposed indicators were carried out targeting disaster-prone areas in 2022(21 areas), and AHP analysis was utilized to determine the relative importance of each indicator. The analysis revealed that the importance of each indicator was as follows: Robustness 0.46, Rapidity 0.22, Redundancy 0.17, and Resourcefulness 0.16, with Robustness exhibiting the highest importance. Regarding the sub-indicators that had the greatest influence on each 4R component, river embankment maintenance emerged as the most influential for Robustness, healthcare services for Rapidity, fiscal autonomy of local governments for Resourcefulness, and drainage facilities for Redundancy.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.775-784
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• 2023

With the increasing trend of extreme rainfall that exceeds the design frequency of man-made structures due to extreme weather, it is necessary to review the safety of agricultural reservoirs designed in the past. However, there are no local government-managed reservoirs (13,685) that can be discharged in an emergency, except for reservoirs over a certain size under the jurisdiction of the Korea Rural Affairs Corporation. In this case, it is important to quickly deploy a mobile siphon to the site for preliminary discharge, and this study evaluated the applicability of a mobile siphon with a diameter of 200 mm, a minimum water level difference of 6 m, 420 (m²/h), and 10,000 (m²/day), which can perform both preliminary and emergency discharge functions, to the Yugum Reservoir in Gyeongju City. The test bed, Yugum Reservoir, is a facility that was completed in 1945 and has been in use for about 78 years. According to the hydrological stability analysis, the lowest height of the current dam crest section is 27.15 (EL.m), which is 0.29m lower than the reviewed flood level of 27.44 (EL.m), indicating that there is a possibility of lunar flow through the embankment, and the headroom is insufficient by 1.72 m, so it was reviewed as not securing hydrological safety. The water level-volume curve was arbitrarily derived because it was difficult to clearly establish the water level-flow relationship curve of the reservoir since the water level-flow measurement was not carried out regularly, and based on the derived curve, the algorithm for operating small and medium-sized old reservoirs was developed to consider the pre-discharge time, the amount of spillway discharge, and to predict the reservoir lunar flow time according to the flood volume by frequency, thereby securing evacuation time in advance and reducing the risk of collapse. Based on one row of 200 mm diameter mobile siphons, the optimal pre-discharge time to secure evacuation time (about 1 hour) while maintaining 80% of the upper limit water level (about 30,000 m²) during a 30-year flood was analyzed to be 12 hours earlier. If the pre-discharge technology utilizing siphons for small and medium-sized old reservoirs and the algorithm for reservoir operation are implemented in advance in case of abnormal weather and the decision-making of managers is supported, it is possible to secure the safety of residents in the risk area of reservoir collapse, resolve the anxiety of residents through the establishment of a support system for evacuating residents, and reduce risk factors by providing risk avoidance measures in the event of a reservoir risk situation.

• Journal of Korea Water Resources Association
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• v.49 no.6
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• pp.519-528
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• 2016

Frequently torrential rain is occurred by climate change and urbanization. Urban is formed with road, residential and underground area. Without detailed topographic flooded analysis consideration can take a result which are wrong flooded depth and flooded area. Especially, flood analysis error of population and assets in dense downtown is causing a big problem for establishments and disaster response of flood measures. It can lead to casualties and property damage. Urban flood analysis is divided into sewer flow analysis and surface inundation analysis. Accuracy is very important point of these analysis. In this study, to confirm the effects of the elevation data precision in the process of flooded analysis were studied using 10m DEM, LiDAR data and 1:1,000 digital map. Study area is Dorim-stream basin in the Darim drainage basin, Sinrim 3 drainage basin, Sinrim 4 drainage basin. Flooding simulation through 2010's heavy rain by using XP-SWMM. Result, from 10m DEM, shows wrong flood depth which is more than 1m. In particular, some of the overflow manhole is not seen occurrence. Accordingly, detailed surface data is very important factor and it should be very careful when using the 10m DEM.

• Fire Science and Engineering
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• v.27 no.3
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• pp.1-7
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• 2013

The wildland fire intensity and scale are getting bigger owing to climate change in the world. In the case of domestic, the forest is distributed over approximately 63.7 % of country and the main facilities like a industrial facility or gas facility abuts onto it. Therefore there is potential that the wildland fire is developed to a large-scale disaster. In this study, the effect distances of the radiant heat flux from the crown fire are analysed according to the change of wind velocity. The safety criteria concerning the radiant heat flux to influence on the surrounding were researched to analyse the effect distances. The criteria of radiant heat flux were chosen $5kW/m^2$, $12.5kW/m^2$, $37.5kW/m^2$. WFDS, which is an extension of NIST's Fire Dynamics Simulator, was used to consequence analysis of the forest fire. In order to apply the analysis conditions, it is researched the forest conditions that is generally distributed in domestic region. As the result, the maximum effect distances by radiant heat were showed at the horizontal and vertical direction. When the wind velocity varied from 0 to 10 m/s, the maximum effect distance increased as the wind velocity increases. Interesting point is that the maximum effect distance were shown at the wind velocity of 8 m/s. The maximum effect distance was decreased according as the fuel moisture of trees increase. This study can contribute to analyse quantitative risk about the damage effect of the surrounding facilities caused by wildland fire.

• International Journal of Highway Engineering
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• v.11 no.3
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• pp.129-139
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• 2009

The transportation network is a critical infrastructure in the event of natural and human caused disasters such as rainfall, snowfall, and terror and so on. Particularly, the transportation network in an urban area where a large number of population live is subject to be negatively affected from such events. Therefore, efficient traffic operation plans are required to assist rapid evacuation and effective detour of vehicles on the network as soon as possible. Recently, ubiquitous communication and sensor network technology is very useful to improve data collection and connection related emergency information. In this study, we develop a specific algorithm to provide evacuation route and detour information only for vehicles under emergency situations. Our algorithm is based on shortest path search technique and dynamic traffic assignment. We perform the case study to evaluate model performance applying hypothetical scenarios involved terror. Results show that the model successfully describe effective path for each vehicle under emergency situation.

• Journal of Wetlands Research
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• v.20 no.2
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• pp.105-115
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• 2018

The frequency analysis of hydrometeorological data is one of the most important factors in response to natural disaster damage, and design standards for a disaster prevention facilities. In case of frequency analysis of hydrometeorological data, it assumes that observation data have statistical stationarity, and a parametric method considering the parameter of probability distribution is applied. For a parametric method, it is necessary to sufficiently collect reliable data; however, snowfall observations are needed to compensate for insufficient data in Korea, because of reducing the number of days for snowfall observations and mean maximum daily snowfall depth due to climate change. In this study, we conducted the frequency analysis for snowfall using the Bootstrap method and SIR algorithm which are the resampling methods that can overcome the problems of insufficient data. For the 58 meteorological stations distributed evenly in Korea, the probability of snowfall depth was estimated by non-parametric frequency analysis using the maximum daily snowfall depth data. The results of frequency based snowfall depth show that most stations representing the rate of change were found to be consistent in both parametric and non-parametric frequency analysis. According to the results, observed data and Bootstrap method showed a difference of -19.2% to 3.9%, and the Bootstrap method and SIR(Sampling Importance Resampling) algorithm showed a difference of -7.7 to 137.8%. This study shows that the resampling methods can do the frequency analysis of the snowfall depth that has insufficient observed samples, which can be applied to interpretation of other natural disasters such as summer typhoons with seasonal characteristics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.927-940
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• 2017

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.