• Journal of the Korean Institute of Landscape Architecture
• /
• v.47 no.4
• /
• pp.12-23
• /
• 2019

This study was carried out to investigate and analyze the environmental characteristics of restoration areas in the riparian zones of the Han River, and to quantify the amount of topsoil organic carbon storage. As a result of investigation and analysis of 21 survey sites, the total number of species planted was found to be 17, and the mean number of species was $2.86{\pm}0.13$ species per site. At least one species and a maximum of 7 species were planted at each site. The mean diameter at breast height was $9.1{\pm}0.6cm$, the mean height was $6.2{\pm}0.3m$ and the root content in soil was $0.13{\pm}0.18g/cm^2$. As a result of the analysis of the soil characteristics, 6 out of 21 items, such as the bulk density, solid ratio, gravel ratio, soil hardness, sand content, and pH increased as the soil layer deepened. The topsoil organic carbon storage by layer was $11.54{\pm}1.08ton/ha$ at 0-10cm, $8.69{\pm}0.81ton/ha$ at 10-20cm, $7.97{\pm}0.79ton/ha$ at 20-30cm, and the total from 0 to 30cm was $28.21{\pm}7.31ton/ha$. The highest amount of topsoil organic carbon storage by land use in the past was $35.17{\pm}5.31ton/ha$ in agricultural lands, followed by $28.16{\pm}8.31ton/ha$ in residential areas, $21.87{\pm}9.05ton/ha$ in commercial areas, $19.23{\pm}12.48ton/ha$ in industrial areas, and $17.07{\pm}11.33ton/ha$ in the barren areas. The highest amount of topsoil organic carbon storage in the restored areas was $38.46{\pm}3.14ton/ha$ in 2006, followed by $28.57{\pm}7.84ton/ha$ in 2016, and $16.78{\pm}6.06ton/ha$ in 2011. The results of this study are expected to provide a basic database and evaluation criteria for enhancing the carbon abatement effects of the restoration sites in riparian zones in the future.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.21 no.4
• /
• pp.64-80
• /
• 2018

In Korea, citizens can only know general information about crime. Thus it is difficult to know how much they are exposed to crime. If the police can predict the crime risky area, it will be possible to cope with the crime efficiently even though insufficient police and enforcement resources. However, there is no prediction system in Korea and the related researches are very much poor. From these backgrounds, the final goal of this study is to develop an automated crime prediction system. However, for the first step, we build a big data set which consists of local real crime information and urban physical or non-physical data. Then, we developed a crime prediction model through machine learning method. Finally, we assumed several possible scenarios and calculated the probability of crime and visualized the results in a map so as to increase the people's understanding. Among the factors affecting the crime occurrence revealed in previous and case studies, data was processed in the form of a big data for machine learning: real crime information, weather information (temperature, rainfall, wind speed, humidity, sunshine, insolation, snowfall, cloud cover) and local information (average building coverage, average floor area ratio, average building height, number of buildings, average appraised land value, average area of residential building, average number of ground floor). Among the supervised machine learning algorithms, the decision tree model, the random forest model, and the SVM model, which are known to be powerful and accurate in various fields were utilized to construct crime prevention model. As a result, decision tree model with the lowest RMSE was selected as an optimal prediction model. Based on this model, several scenarios were set for theft and violence cases which are the most frequent in the case city J, and the probability of crime was estimated by $250{\times}250m$ grid. As a result, we could find that the high crime risky area is occurring in three patterns in case city J. The probability of crime was divided into three classes and visualized in map by $250{\times}250m$ grid. Finally, we could develop a crime prediction model using machine learning algorithm and visualized the crime risky areas in a map which can recalculate the model and visualize the result simultaneously as time and urban conditions change.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.21 no.1
• /
• pp.29-41
• /
• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Wetlands Research
• /
• v.26 no.1
• /
• pp.41-50
• /
• 2024

Plant Microbial Fuel Cells (P-MFCs) are biomass-based energy technologies that generate electricity from plant and root microbial communities and are suitable for natural fundamental solutions considering sustainable environments. In order to develop P-MFC technology suitable for domestic waterfront space, it is necessary to analyze international research trends first. Therefore, in this study, 700 P-MFC-related research papers were investigated in Web of Science, and the core keywords were derived using VOSviewer, a word analysis program, and the research trends were analyzed. First, P-MFC-related research has been on the rise since 1998, especially since the mid to late 2010s. The number of papers submitted by each country was "China," "U.S." and "India." Since the 2010s, interest in P-MFCs has increased, and the number of publications in the Philippines, Ukraine, and Mexico, which have abundant waterfront space and wetland environments, is increasing. Secondly, from the perspective of research trends in different periods, 1998-2015 mainly carried out microbial fuel cell performance verification research in different environments. The 2016-2020 period focuses on the specific conditions of microbial fuel cell use, the structure of P-MFC and how it develops. From 2021 to 2023, specific research on constraints and efficiency improvement in the development of P-MFC was carried out. The P-MFC-related international research trends identified through this study can be used as useful data for developing technologies suitable for domestic waterfront space in the future. In addition to this study, further research is needed on research trends and levels in subsectors, and in order to develop and revitalize P-MFC technologies in Korea, research on field applicability should be expanded and policies and systems improved.