• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1723-1735
• /
• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Korean Society of Disaster and Security
• /
• v.14 no.2
• /
• pp.1-11
• /
• 2021

Recently, in Korea, the risk of meteorological disasters is increasing due to climate change, and the damage caused by rainfall is being emphasized continuously. Although the current weather forecast provides quantitative rainfall, there are several difficulties in predicting the extent of damage. Therefore, in order to understand the impact of damage, the threshold rainfall for each watershed is required. The damage caused by rainfall occurs differently by region, and there are limitations in the analysis considering the characteristic factors of each watershed. In addition, whenever rainfall comes, the analysis of rainfall-runoff through the hydrological model consumes a lot of time and is often analyzed using only simple rainfall data. This study used GIS data and calculated the threshold rainfall from the threshold runoff causing flooding by coupling two hydrologic models. The calculation result was verified by comparing it with the actual case, and it was analyzed that damage occurred in the dangerous area in general. In the future, through this study, it will be possible to prepare for flood risk areas in advance, and it is expected that the accuracy will increase if machine learning analysis methods are added.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.1
• /
• pp.42-54
• /
• 2019

Crop models depend on a large number of input parameters including the cultivar parameters that represent the genetic characteristics of a given cultivar. The cultivar parameters have been estimated using high quality data for crop growth, which require considerable costs and efforts. The objective of this study was to examine the feasibility of using low quality data for the parameter estimation. In the present study, the cultivar parameters for cv. Shindongjin were estimated using the data obtained from the report of new cultivars development and research from 2005 to 2016. The root mean square errors (RMSE) of the heading dates were less than 3 days when the parameters associated with phenology were estimated. In contrast, the coefficient of determination for yield tended to be less than 0.1. The large errors incurred by the fact that no growth data collected over a season was used for parameter estimation. This suggests that detailed observation data needs to be prepared for parameter calibration, which would be aided by remote sensing approaches. The occurrence of natural disasters during a growing season has to be considered because crop models cannot take into account the effects of those events. Still, our results provide a reasonable range for the parameters, which could be used to set the boundary of a given parameter for cultivars similar to cv. Shindongjin in further studies.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1633-1641
• /
• 2022

Urban population concentration and indiscriminate development are causing various environmental problems such as air pollution and heat island phenomena, and causing human resources to deteriorate the damage caused by natural disasters. Urban trees have been proposed as a solution to these urban problems, and actually play an important role, such as providing environmental improvement functions. Accordingly, quantitative measurement and analysis of individual trees in urban trees are required to understand the effect of trees on the urban environment. However, the complexity and diversity of urban trees have a problem of lowering the accuracy of single tree detection. Therefore, we conducted a study to effectively detect trees in Dongjak-gu using high-resolution aerial images that enable effective detection of tree objects and You Only Look Once Version 5 (YOLOv5), which showed excellent performance in object detection. Labeling guidelines for the construction of tree AI learning datasets were generated, and box annotation was performed on Dongjak-gu trees based on this. We tested various scale YOLOv5 models from the constructed dataset and adopted the optimal model to perform more efficient urban tree detection, resulting in significant results of mean Average Precision (mAP) 0.663.

• The Journal of Bigdata
• /
• v.7 no.2
• /
• pp.1-9
• /
• 2022

In the global era, the importance of imported food safety management is increasing. Address information of overseas food companies is key information for imported food safety management, and must be verified for prompt response and follow-up management in the event of a food risk. However, because each country's address system is different, one verification system cannot verify the addresses of all countries. Also, the purpose of address verification may be different depending on the field used. In this paper, we deal with the problem of classifying a given overseas food business address into the administrative district level of the country. This is because, in the event of harm to imported food, it is necessary to find the administrative district level from the address of the relevant company, and based on this trace the food distribution route or take measures to ban imports. However, in some countries the administrative district level name is omitted from the address, and the same place name is used repeatedly in several administrative district levels, so it is not easy to accurately classify the administrative district level from the address. In this study we propose a deep learning-based administrative district level classification model suitable for this case, and verify the actual address data of overseas food companies. Specifically, a method of training using a label powerset in a multi-label classification model is used. To verify the proposed method, the accuracy was verified for the addresses of overseas manufacturing companies in Ecuador and Vietnam registered with the Ministry of Food and Drug Safety, and the accuracy was improved by 28.1% and 13%, respectively, compared to the existing classification model.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.19 no.2
• /
• pp.43-53
• /
• 2017

This study aims to analyze the effect of typhoons on pear production. Pears are typical fruits that are vulnerable to typhoon damages, so typhoons are negatively associated with pear productivity. However, relatively less pear damages by typhoons in the main pear production area, comparing to the average in Korea, have been reported. The main production area seems to adopt better agricultural techniques or practices to cope with natural disasters such as typhoons. Thus, this study tests the hypothesis that there are differences of production losses due to typhoons between the main pear production area and the rest using the stochastic frontier analysis. The main production area is defined by Location Quotient Index (LQI), and we found that LQI had a significant effect to decrease the productivity losses in the main production areas, which shows that those production areas alleviated the pear production loss due to typhoons.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• 2004.11a
• /
• pp.596-599
• /
• 2004

Recently great construction companies strive for utilization of integrated safety management information in advanced IT in many fields. And construction companies have a interest and effort to construct system for integration and management of safety management information. Some earlier companies are appling to construction site ,but that is limited great construction company But it need to solve the origin problem because of it continually keep up the rate of disaster. Namely management system need more manage subcontractor that safety management in construction site. This purpose of study is an offer of foundation data to introduce and improve of safety management Information system. The paper is supposed to choose the essential regular items and going to propose improvement of safety information system according to the survey

• Korean Journal of Remote Sensing
• /
• v.31 no.2
• /
• pp.183-191
• /
• 2015

If the damage analysis on river-side facilities such as dam, river bank structures and bridges caused by disasters such as typhoon, flood, etc. becomes available, it can be a great help for disaster recovery and decision-making. In this research, We tried to extract a Digital Surface Model (DSM) and analyze the accuracy from Unmanned Air Vehicle (UAV) images on river-side facilities. We tried to apply stereo image-based matching technique, then extracted match results were united with one mosaic DSM. The accuracy was verified compared with a DSM derived from LIDAR data. Overall accuracy was around 3m of absolute and root mean square error. As an analysis result, we confirmed that exterior orientation parameters exerted an influence to DSM accuracy. For more accurate DSM generation, accurate EO parameters are necessary and effective interpolation and post process technique needs to be developed. And the damage analysis simulation with DSM has to be performed in the future.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2012.05a
• /
• pp.199-199
• /
• 2012

기후변화로 인한 기상재해의 피해가 전 세계적으로 계속 증가하고 있으며, 특히 기후변화로 인한 집중호우는 시민들의 안전, 재산, 인명피해를 일으키고 있다. 이러한 피해를 최소화하기 위해서는 한 신뢰성 높은 미래 기후시나리오가 필수적이며, 미래 기후시나리오를 바탕으로 하여 기후변화로 인한 향후 발생할 수 있는 위험성의 정도를 전망하여 적응대책을 수립할 필요가 있다. 본 연구에서는 기후시나리오를 이용하여 한국의 미래 강수량변화를 전망한다. 본 연구를 수행하기 위하여, A2시나리오의 ECHO-G/S에서 생산된 기후 시나리오를 이용하여 지역 기후모델인 RegCM3에 적용하여 기후 시나리오를 생산하였다. RegCM3에서 생산된 기후시나리오는 Sub-BATS라는 기법을 이용하여 한반도 5km 해상도의 기후시나리오가 생산되었다. 또한 생산된 기후시나리오와 비교분석을 위하여 전구 20km 해상도의 기후시나리오를 이용하여 미래 강수량 변화를 각 계급별로 분석 하였다. 역학적 상세화 방법에 의해 생산된 기후시나리오는 전체적으로 과소 추정되는 경향이 크게 나타나고 있었으며, 일강수량의 경우 관측자료 보다 상당히 작게 나타나는 특징이 있었다. 역학적 상세화에 의한 강수량은 기존의 연구에서도 비슷한 특징이 나타나고 있었다. 결과적으로 역학적 상세와에 의한 기후시나리오를 이용하여 미래 강수량 변화를 분석하는 것은 강수량의 경향성을 분석할 수는 있지만 정량적으로 분석하는 것에는 한계가 있다. 전구 20km 해상도의 기후시나리오는 전체적으로 관측자료와 상당히 유사하게 모의되고 있었으며, 일강수량 또한 상당히 유사하게 나타나고 있었다. 전구 20km 해상도의 기후시나리오를 이용하여 미래 강수량을 분석한 결과, 전반적으로 증가하는 경향이 있었으며, 21세기 후반에는 약 18%의 연강수량 증가가 나타났으며, 그 중에서도 겨울철의 강수량 증가가 38%로 가장 크게 나타났다. 강수일수 변화는 약 5mm이하는 감소하는 경향이 있었으며, 5mm 이상은 증가하는 경향이 나타났다. 그리고 각 계급별 강수량 변화는, 상대적으로 강수량이 적은 10, 30mm/day는 여름철에 비해 겨울철에 강수량 증가가 크게 나타나고 있었으며, 상대적으로 강수량이 큰 50, 80, 100, 130mm/day는 겨울철에 비해 여름철에 강수량 증가가 크게 나타났다. 본 연구에서 나타난 결과는 미래 수자원 영향평가 및 적응대책에 유용하게 쓰일 것이다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.126-126
• /
• 2018

최근 우리나라는 기후변화로 인한 이상기후 현상 중 가뭄에 대한 발생빈도가 증가하고 있다. 가뭄은 다른 자연재해에 비해 지속기간이 길고 규모가 광범위하여, 사회 경제적인 피해가 크게 발생한다. 이러한 가뭄에 대비하기 위해서는 지역적으로 적합한 가뭄 대책을 수립해야 하며, 이를 위해서는 가뭄 위험도 평가가 선행되어야 한다. 지역적 가뭄 위험도를 평가하기 위해서는 기상학적 요인뿐만 아니라 사회 경제적인 요인에 의한 영향을 고려하는 가뭄 취약성 평가가 수반되어야 한다. 본 연구에서는 지역별 가뭄 취약성 평가를 수행하기 위해, 지역별 용수 수요 및 공급관련 인자와 선행연구에서 정의된 가뭄 위험인자들 중 8개(생활 농업 공업 용수공급량, 인구밀도, 1인당 가용수자원량, 물 자급률, 취수율, 물 이용 공평성)를 선택하였다. 베이지안 네트워크(Bayesian Network) 기법을 통해 선정된 사회 경제적 요인들과 가뭄과의 상관관계를 분석하여 각 지역의 특성을 고려한 가뭄 위험요인별 확률을 산정하였다. 최종적으로 산정된 주요 가뭄 위험요인별 확률을 우선순위에 따른 가중치를 적용하여 지역별 가뭄 취약성지수(Drought Vulnerability Index, DVI)를 산정하였고, 이를 이용하여 우리나라의 행정구역별로 취약성 평가를 수행하고 지도로 표시하였다. 지역별 가뭄 취약성 평가를 수행한 결과 익산, 상주, 완주 순으로 높게 나타났으며, 계룡, 과천, 종로순으로 가장 낮게 산정되었다. 또한 광역자치단체의 평균 가뭄 취약성지수를 산정한 결과 전라북도 지역이 가장 높게 나타났으며, 대구 및 대전광역시가 가장 낮게 나타났다.