• Title/Summary/Keyword: spatial regression

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Development of High-frequency Data-based Inflow Water Temperature Prediction Model and Prediction of Changesin Stratification Strength of Daecheong Reservoir Due to Climate Change (고빈도 자료기반 유입 수온 예측모델 개발 및 기후변화에 따른 대청호 성층강도 변화 예측)

  • Han, Jongsu;Kim, Sungjin;Kim, Dongmin;Lee, Sawoo;Hwang, Sangchul;Kim, Jiwon;Chung, Sewoong
    • Journal of Environmental Impact Assessment
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    • v.30 no.5
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    • pp.271-296
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    • 2021
  • Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

Observation of Ice Gradient in Cheonji, Baekdu Mountain Using Modified U-Net from Landsat -5/-7/-8 Images (Landsat 위성 영상으로부터 Modified U-Net을 이용한 백두산 천지 얼음변화도 관측)

  • Lee, Eu-Ru;Lee, Ha-Seong;Park, Sun-Cheon;Jung, Hyung-Sup
    • Korean Journal of Remote Sensing
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    • v.38 no.6_2
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    • pp.1691-1707
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    • 2022
  • Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

Predicting the Effects of Rooftop Greening and Evaluating CO2 Sequestration in Urban Heat Island Areas Using Satellite Imagery and Machine Learning (위성영상과 머신러닝 활용 도시열섬 지역 옥상녹화 효과 예측과 이산화탄소 흡수량 평가)

  • Minju Kim;Jeong U Park;Juhyeon Park;Jisoo Park;Chang-Uk Hyun
    • Korean Journal of Remote Sensing
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    • v.39 no.5_1
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    • pp.481-493
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    • 2023
  • In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

A Comparative Study on Factors Affecting Satisfaction by Travel Purpose for Urban Demand Response Transport Service: Focusing on Sejong Shucle (도심형 수요응답 교통서비스의 통행목적별 만족도 영향요인 비교연구: 세종특별자치시 셔클(Shucle)을 중심으로)

  • Wonchul Kim;Woo Jin Han;Juntae Park
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.23 no.2
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    • pp.132-141
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    • 2024
  • In this study, the differences in user satisfaction and the variables influencing the satisfaction with demand response transport (DRT) by travel purpose were compared. The purpose of DRT travel was divided into commuting/school and shopping/leisure travel. A survey conducted on 'Shucle' users in Sejong City was used for the analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis was applied to minimize the overfitting problems of the multilinear model. The results of the analysis confirmed the possibility that the introduction of the DRT service could eliminate the blind spot in the existing public transportation, reduce the use of private cars, encourage low-carbon and public transportation revitalization policies, and provide optimal transportation services to people who exhibit intermittent travel behaviors (e.g., elderly people, housewives, etc.). In addition, factors such as the waiting time after calling a DRT, travel time after boarding the DRT, convenience of using the DRT app, punctuality of expected departure/arrival time, and location of pickup and drop-off points were the common factors that positively influenced the satisfaction of users of the DRT services during their commuting/school and shopping/leisure travel. Meanwhile, the method of transfer to other transport modes was found to affect satisfaction only in the case of commuting/school travel, but not in the case of shopping/leisure travel. To activate the DRT service, it is necessary to consider the five influencing factors analyzed above. In addition, the differentiating factors between commuting/school and shopping/leisure travel were also identified. In the case of commuting/school travel, people value time and consider it to be important, so it is necessary to promote the convenience of transfer to other transport modes to reduce the total travel time. Regarding shopping/leisure travel, it is necessary to consider ways to create a facility that allows users to easily and conveniently designate the location of the pickup and drop-off point.