• Journal of the Korean Data and Information Science Society
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• 제28권1호
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• pp.29-37
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• 2017

산림경영 계획을 위한 필요한 산림재적을 보다 효율적으로 추정하기 위해서 다양한 연구가 요구되어져 왔는데, 이러한 산림구조에 관한 연구는 주로 현장조사와 위성영상을 이용하여 이루어진다. 현장조사를 통한 연구는 비교적 정확하나 시간과 비용이 많이 들 뿐 아니라 접근의 용이성이 떨어지는 지역이 있기 때문에, 넓은 지역의 조사가 어렵다는 단점이 있다. 최근에는 항공기에서 발사된 레이저 펄스가 반사되어 돌아오는 시간을 측정하여 대상의 3차원 좌표를 얻는 LiDAR (Light Detection and Ranging) 기술을 활용하여 획득한 정밀한 수치형자료를 이용한 산림의 구조에 관한 연구가 이루어지고 있다. 일반적으로 산림재적을 추정하기 위해서 LiDAR자료를 이용한 수고자료와 산림 재적에 대한 회귀모형의 중요성이 점차 높아지는데, 국내의 경우 수목의 종류와 그 분포가 다르기 때문에 회귀모형만으로 재적을 추정하는 데 한계가 있다. 따라서 본 논문에서는 산림의 수고와 흉고직경을 측정하여 재적값을 추정하고 산림의 공간효과를 고려한 계층적 베이지안 분석을 통해 관측되지 않은 전체 산림재적에 대한 추정을 하고자 한다.

• 한국지구과학회지
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• 제34권4호
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• pp.336-344
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• 2013

이 연구는 북서태평양에서 여름철(7-9월) 동안 발생하는 태풍 빈도를 예측하기 위한 다중회귀모델을 4가지 원격패턴을 이용하여 개발하였다. 이 패턴은 4-5월 동안 동아시아 대륙에서의 시베리아 고기압 진동, 북태평양에서의 북태평양 진동, 호주근처의 남극진동, 적도 중앙태평양에서의 대기순환으로 대표된다. 이 통계모델은 이 모델로부터 예측된 높은 태풍발생빈도의 해와 낮은 태풍발생빈도의 해 사이에 차를 분석함으로써 검증되었다. 높은 태풍발생빈도의 해에는 다음과 같은 4가지의 아노말리 특성을 나타내었다: i) 동아시아 대륙에 고기압성 순환 아노말리(양의 시베리아 고기압진동), ii) 북태평양에 남저북고의 기압계 아노말리, iii) 호주 근처에 저기압성 순환 아노말리(양의 남극진동), iv) 봄부터 여름 동안 니뇨3.4 지역에 저기압성 순환 아노말리. 따라서 적도 서태평양에서 무역풍 아노말리는 양반구의 아열대 서태평양에 위치한 저기압성 순환 아노말리에 의해 약화되었다. 결국, 이러한 기압계 아노말리의 공간분포는 열대 서태평양에 대류를 억제하는 대신 아열대 서태평양에 대류를 강화시켰다.

• 한국지형학회지
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• 제26권3호
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• pp.53-67
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• 2019

The purpose of this study is to identify the quality of landslide susceptibility in a landslide-prone area (Jinbu-myeon, Gangwon-do, South Korea) by spatial prediction modeling approach and compare the results obtained. For this goal, a landslide inventory map was prepared mainly based on past historical information and aerial photographs analysis (Daum Map, 2008), as well as some field observation. Altogether, 550 landslides were counted at the whole study area. Among them, 182 landslides are debris flow and each group of landslides was constructed in the inventory map separately. Then, the landslide inventory was randomly selected through Excel; 50% landslide was used for model analysis and the remaining 50% was used for validation purpose. Total 12 contributing factors, such as slope, aspect, curvature, topographic wetness index (TWI), elevation, forest type, forest timber diameter, forest crown density, geology, landuse, soil depth, and soil drainage were used in the analysis. Moreover, to find out the co-relation between landslide causative factors and incidents landslide, pixels were divided into several classes and frequency ratio for individual class was extracted. Eventually, six landslide susceptibility maps were constructed using the Bayesian Predictive Discriminant (BPD), Empirical Likelihood Ratio (ELR), and Linear Regression Method (LRM) models based on different category dada. Finally, in the cross validation process, landslide susceptibility map was plotted with a receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) and tried to extract success rate curve. The result showed that Bayesian, likelihood and linear models were of 85.52%, 85.23%, and 83.49% accuracy respectively for total data. Subsequently, in the category of debris flow landslide, results are little better compare with total data and its contained 86.33%, 85.53% and 84.17% accuracy. It means all three models were reasonable methods for landslide susceptibility analysis. The models have proved to produce reliable predictions for regional spatial planning or land-use planning.

• 한국수자원학회논문집
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• 제43권12호
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• pp.1083-1091
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• 2010

분포형 수문 모형의 일강우 입력 자료는 불가피하게 불규칙하고 밀도가 낮은 관측망에서 기록된 값을 내삽해 사용하게 되나, 흔히 사용되는 대부분의 내삽법들은 실제 일강우의 다양한 공간적 분포를 잘 재현하지 못하는 문제가 있다. 본 연구에서는 널리 사용되는 다섯 가지의 강우 내삽 방법을 두개의 유역에 사용하여 비교하고 실제 공간적 분포를 보다 잘 나타낼 수 있는 2단계 내삽법을 제안하였다. 비교에 사용된 내삽법은 (1) 역가중치 방법(IDW), (2) 다중회귀분석 (MLR), (3) 월강우를 이용한 다중회귀분석법(CMLR), (4) 국지가중치 다중회귀분석(LWP) 등이다. 보다 향상된 내삽을 위한 2단계 내삽법은 먼저 로지스틱 회귀분석으로 강우-비강우 지역을 구분하고 강우 지역에서만 기존의 내삽법을 적용하여 강우량을 구하는 방법이다. 기존 방법과의 비교결과 공간적인 편차가 심한 일강우의 특성을 2단계 내삽법에서 잘 표현하고 있는 것으로 나타났다. 제안된 방법은 수문모형에의 적용뿐만 아니라 유출량의 예보 및 대기 순환 모형의 다운 스케일링에도 효과적으로 사용될 수 있을 것으로 기대된다.

• 생태와환경
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• 제42권4호
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• pp.452-464
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• 2009

The objectives of this study were to evaluate spatial and temporal variability of water quality in 10 reservoirs and identify the key nutrients (N, P) influencing chlorophyll-a (CHL) along with analysis of empirical models and zonal patterns of total phosphorus (TP) and CHL. We analyzed total nitrogen (TN), TP, CHL, water clarity (Secchi depth, SD), and evaluated potential limiting nutrient using ambient N:P ratios and previous criteria of ambient nutrients. Water clarity and CHL varied largely depending on the seasonal monsoon and type of reservoir, but trophic state was diagnosed as eutrophy, base on mean CHL in most reservoirs. The peak of TP did not match the contents of CHL due to rapid flushing during the high run-off period. In the reservoir of DR, regression coefficient in the $P_r$ was 0.510 but was 0.159 in the $M_o$, while the TP-CHL relation in the YR increased during the monsoon compared to the premonsoon. The regression coefficient in the $P_r$ was not statistically significant but the value of $M_o$ was 0.250. TP showed similar longitudinal zonal gradients among the reservoirs of DR, YR and JR. Empirical models of TP-CHL, based on overall data, showed that CHL was determined by phosphorus($R^2=0.244$, p=0.0019). Regression analysis of CHL-SD showed a stronger linear fit ($R^2=0.638$, p<0.001) than the TP-CHL model.

• 대한원격탐사학회지
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• 제30권1호
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• pp.61-73
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• 2014

본 연구는 항공 초분광영상을 사용한 수질추정 활용을 검토하고 한강일부분에 대해 가용한 측정자료를 이용하여 초분광영상 기반의 수질추정을 테스트하였다. 원격탐사에 의한 수질추정은 수체에 대한 downwelling과 수체 내에서의 산란과 반사에 대한 관측정보를 이용하는 방법과 원격탐사 센서에 도달하는 upwelling과 수질측정정보와의 선형적 회귀분석을 구하는 방법이 선호된다. 두 방법 모두 유의미한 결과를 도출하지만 수질정보나 산란정보 등 추정에 필요한 보조자료에 의한 영향이 더 클 것으로 판단되었다. 수질 추정 테스트는 팔당댐 하류에 위치한 한강의 일부분에 대해서 적용되었다. AISA eagle 초분광센서로 취득된 자료와 수질관측정보를 선형적 회귀분석을 통한 방법을 적용하였다. 기존 문헌에서 제시된 밴드조합에 대해서 회귀분석한 결과 유의미한 밴드조합으로 $-24.847+0.013L_{560}$의 회귀식을 얻었다 ($L_{560}$은 560 nm 파장에서의 radiance로 $R^2$=0.985). 다중분광영상을 이용했을 경우의 결과와 비교하기 위해서 spectral resampling을 통해 Landsat TM 영상을 생성하여 -55.932 + 33.881(TM, TM3)의 회귀식을 얻을 수 있었다(TM, TM3는 radiance로, $R^2$=0.968). 부유물질 농도는 수질측정지점에서 약 3.75 mg/l 이고, 초분광영상으로 추정된 농도는 약 3.65 mg/l, 시뮬레이션된 TM은 약 5.85 mg/l 로 다중분광영상을 이용했을 경우 과대 추정하는 경향을 보였다. 항공 초분광영상의 활용가치를 높이고 보다 정밀한 값을 추정하기 위해서 영상 전반에 걸친 sun glint 와 같은 영향을 최소화하기 위해 태양고도각을 고려하여 정교한 비행계획을 구성하고 체계적 전처리와 검 보정 체계를 갖출 필요가 있다고 사료된다. 일반적으로 적용된 방법에 따른 테스트로, 대기보정의 정밀성과 부족한 수질측정 샘플자료, 분광밴드의 검색, 적합한 선형회귀모델의 선택, 그리고 정량적 검증방법과 같은 몇 가지 문제점과 제약사항들을 발견할 수 있었다.

• 한국공간구조학회논문집
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• 제23권3호
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• pp.87-94
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• 2023

Chloride is one of the most common threats to reinforced concrete (RC) durability. Alkaline environment of concrete makes a passive layer on the surface of reinforcement bars that prevents the bar from corrosion. However, when the chloride concentration amount at the reinforcement bar reaches a certain level, deterioration of the passive protection layer occurs, causing corrosion and ultimately reducing the structure's safety and durability. Therefore, understanding the chloride diffusion and its prediction are important to evaluate the safety and durability of RC structure. In this study, the chloride diffusion coefficient is predicted by machine learning techniques. Various machine learning techniques such as multiple linear regression, decision tree, random forest, support vector machine, artificial neural networks, extreme gradient boosting annd k-nearest neighbor were used and accuracy of there models were compared. In order to evaluate the accuracy, root mean square error (RMSE), mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2) were used as prediction performance indices. The k-fold cross-validation procedure was used to estimate the performance of machine learning models when making predictions on data not used during training. Grid search was applied to hyperparameter optimization. It has been shown from numerical simulation that ensemble learning methods such as random forest and extreme gradient boosting successfully predicted the chloride diffusion coefficient and artificial neural networks also provided accurate result.

• 한국공간구조학회논문집
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• 제23권4호
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• Journal of Ecology and Environment
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• 제40권1호
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• pp.34-44
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• 2016

Background: Little is known about how chemical water quality is associated with ecological stream health in relation to landuse patterns in a watershed. We evaluated spatial characteristics of water quality characteristics and the ecological health of Dongjin-River basin, Korea in relation to regional landuse pattern. The ecological health was assessed by the multi-metric model of Index of Biological Integrity (IBI), and the water chemistry data were compared with values obtained from the health model. Results: Nutrient and organic matter pollution in Dongjin-River basin, Korea was influenced by land use pattern and the major point sources, so nutrients of TN and TP increased abruptly in Site 4 (Jeongeup Stream), which is directly influenced by wastewater treatment plants along with values of electric conductivity (EC), bacterial number, and sestonic chlorophyll-a. Similar results are shown in the downstream (S7) of Dongjin River. The degradation of chemical water quality in the downstream resulted in greater impairment of the ecological health, and these were also closely associated with the landuse pattern. Forest region had low nutrients (N, P), organic matter, and ionic content (as the EC), whereas urban and agricultural regions had opposite in the parameters. Linear regression analysis of the landuse (arable land; $A_L$) on chemicals indicated that values of $A_L$ had positive linear relations with TP ($R^2=0.643$, p < 0.01), TN ($R^2=0.502$, p < 0.05), BOD ($R^2=0.739$, p < 0.01), and suspended solids (SS; ($R^2=0.866$, p < 0.01), and a negative relation with TDN:TDP ratios ($R^2=0.719$, p < 0.01). Conclusions: Chemical factors were closely associated with land use pattern in the watershed, and these factors influenced the ecological health, based on the multimetric fish IBI model. Overall, the impairments of water chemistry and the ecological health in Dongjin-River basin were mainly attributes to point-sources and land-use patterns.

• 한국농공학회논문집
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• 제58권2호
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• pp.53-63
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• 2016

Monitoring of cyanobacteria bloom in reservoir systems is important for water managers responsible of water supply system. Cyanobacteria affect the taste and smell of water and pose considerable filtration problems at water use places. Harmful cyanobacteria bloom in reservoir have significant economic impacts. We develop a new method for estimating the cyanobacteria bloom using Landsat TM and ETM+ data. Developed model was calibrated and cross-validated with existing in situ measurements from Daecheong Reservoir's Water Quality Monitoring Program and Algae Alarm System. Measurements data of three stations taken from 2004 to 2012 were matched with radiometrically converted reflectance data from the Landsat TM and ETM+ sensor. Stepwise multiple linear regression was used to select wavelengths in the Landsat TM and ETM+ bands 1, 2 and 4 that were most significant for predicting cyanobacteria cell number and bio-volume. Based on statistical analysis, the linear models were that included visible band ratios slightly outperformed single band models. The final monitoring models captured the extents of cyanobacteria blooms throughout the 2004-2012 study period. The results serve as an added broad area monitoring tool for water resource managers and present new insight into the initiation and propagation of cyanobacteria blooms in Daecheong reservoir.