• Journal of Korean Society on Water Environment
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• v.35 no.3
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• pp.257-270
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• 2019

Followingthe Four Rivers Project, cyanobacterial blooms have been frequently observed in the upstream of the installed weirs. The aim of this study was to characterize the major environmental factors that are associated with the cyanobacteria dominance in Baekje Weir (Geum River) and Juksan Weir (Youngsan River) based on intensive experiments and systematic data mining methods. The factors related to the cyanobacteria dominance include7-days cumulative rainfall (APRCP7), 7-days averaged flow (Q7day), water temperature (Temp), stratification strength (${\Delta}T$), electronic conductivity (EC), DO, pH, $NO_3-N$, $NH_3-N$, TN, TP, $PO_4-P$, Chl-a, Fe, BOD, COD, TOC, and $SiO_2$. The most highly correlatedfactors to the dominant cyanobacteria were found to be EC, Temp, Q7day, $PO_4-P$ in theBaekje Weir. On the other hand, those dominant in the Juksan Weir were ${\Delta}T$, TOC, Temp, EC and TN. The EC showed a strong correlation with cyanobacteria dominance in both weirs because a high EC represents a persisted low flow condition. The cyanobacteria dominance was as high as 56 % when the EC was equal or greater than $418{\mu}S/cm$ in Baekje Weir. It was as high as 63% when the ${\Delta}T{\geq}2.1^{\circ}C$ in the Juksan Weir. However, nutrients showed a minor correlation with cyanobacteria dominance in both weirs. The results suggest that the cyanobacteria dominate in astate where the water flow rate is low, water temperature is high and thermal stratification is strengthened. Therefore, the improvement of flow regimes is the most important to prevent persistent thermal stratification and formation of cyanobacteria bloom in theBaekje and JuksanWeirs.

• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.438-445
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• 2020

The characteristics of forest environmental factors were analyzed using the quantification theory (I) for prediction of the suitable area of erosion control dams. The results indicated that sediment discharge in small forest catchments was significantly correlated with dredging passage (0.7495) and age class (0.6000). In contrast, area (0.3416), slope gradient (0.3207), rainfall (0.3160), altitude (0.2990) and soil type (0.2192) were poorly correlated. Following quantification theory (I), we developed a selection decision table for erosion control dams based on sediment discharge rate as class I (highly suitable site, greater than 2.2496), class II (suitable site, 1.1248~2.2495), and class III (poorly suited site, lower than 1.1247).

• Journal of Korean Society of Water Science and Technology
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• v.26 no.6
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• pp.133-142
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• 2018

Increasing localized torrential rainfall caused by abnormal climate are making higher damage to human and property through urban inundation So The need of preventive measures is being highlighted. In this study, the methodology for calculating flood depth in domestic water map using an interpolation method in order to utilizing the results of flood analysis provided only in the form of a report is suggested. In the Incheon Metropolitan City S area as the test-bed, the flood depth was calculated using the interpolating the actual flood analysis by image and verification was performed. Verification results showed that the error rate was 5.2% for the maximum flooding depth, and that the water depth value was compared to 10 random points, which showed a difference of less than 0.030 m. Also, as the results of the flood analysis were presented in various ways, the flood depth was extracted from the image of the result of the flood analysis, which changed the presentation method, and then compared and analyzed. The results of this study could be available for the use of basic data from the research on the urban penetration of domestic consumption and for decision-making of policy.

• Journal of Korean Society of Disaster and Security
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• v.14 no.4
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• pp.37-45
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• 2021

Flooding damage may occur due to an unexpected increase in rainfall in summer. Previously, the roughness coefficient, which is a major factor of conveyance, was calculated through on-site measurement, but in case of on-site measurement, there are many limits in accurately grasping changes in vegetation. In this study, the vegetation index (NDVI) was calculated using the Sentinel-2 optical images, and the modified roughness coefficient was calculated through the density and distribution area of the vegetation. Then the calculated roughness coefficient was applied to HEC-RAS 1D model and verified by comparing the results with the water level at the water level station directly downstream of the Soyang River dam. As a result, the error rate of the water level decreased about 14% compared to applying the previous roughness coefficient. Through this, it is expected that it will be possible to refine the flood level of rivers in consideration of seasonal flood characteristics and to efficiently maintain rivers in specific sections.

• Journal of the Korean Geotechnical Society
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• v.38 no.6
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• pp.41-47
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• 2022

Permeable pavement technology allows the penetration of rainfall into the roadbed, thereby reducing surface runoff and enhancing water quality. The water quality can be improved by adding a filter layer to the permeable pavement. This study analyzes the permeability performance and particulate-matter removal efficiency of a bottom ash-silica sand filter. The performances of five filters with bottom ash and silica sand as the basic materials were evaluated on particulate matter sized 60 ㎛ or smaller. The pure silica sand sample and pure bottom ash sample delivered an average removal efficiency of around 70%. The removal efficiency of the mixed sample was approximately 90%, exceeding the recommended reduction rate (80%) at non-point pollution reduction facilities. In future work, the filter performance should be further verified on permeable pavement.

• Journal of Forest and Environmental Science
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• v.26 no.2
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• pp.113-116
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• 2010

The Acacia seyal Del. var. seyal belongs to family Mimosaceae is known locally as gum Talha tree. It is a multipurpose tree species occurs throughout the African gum belt in Savannah mostly in pure forest. In Sudan it thrives on heavy clay soils that receive an annual rainfall between 400-800 mm. It is an important source of rural energy (fuelwood and charcoal) and forage. As mentioned by Nair (2007) the economic damage causes by insect in natural forest often difficult to judge due to no enough research attention The tree is frequently affected by biotic factors among them the insect pests. During a survey in the 1980's the tree was severely infested by the longhorned beetles (Cerambycidae) severely infesting other Acacia species, but the ecological data are overlooked. Therefore, the objective of the study was to assess infestation characteristics and to determine environmental factors triggering the attack of longhorned beetles. A temporary random sampling technique was applied to observe the damage characteristics of the longhorned beetles on tree species during May-July 2007. Five sample plots occupies by A. seyal were taken in Kordofan region directly observed for the presence of hole of emergence of the longhorned beetles, presence of dusts, presence of insect stages, girdling as well as other characteristics of damage. The study results indicate that the infestation rate of trees in the sampled sites ranged between zero and 23.08%. Further ecological researches are recommended.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.

• Journal of the Korean GEO-environmental Society
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• v.24 no.5
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• pp.21-28
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• 2023

In this paper, an experimental study was conducted to present the properties of rapid hardening composite mat, and a numerical analysis was carried out to analyze the slope protection effect of the mats based on ground conditions, rainfall, slope gradient and soil height. As a result, the application of rapid hardening composite mat increased the slope safety factor in all conditions, and the increase rate of safety factor showed an average of 40% increase both in dry and rainy seasons. Through these research findings, the protective effect of the rapid hardening composite mat on sloping surfaces has been proven, and it is suggested that the rapid hardening composite mat is suitable for application in areas where slope failure or collapse is expected.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.1
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• pp.15-26
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• 2023

This study aimed to evaluate the performance of water level classification from CCTV images in agricultural facilities such as reservoirs. Recently, the CCTV system, widely used for facility monitor or disaster detection, can automatically detect and identify people and objects from the images by developing new technologies such as a deep learning system. Accordingly, we applied the ResNet-50 deep learning system based on Convolutional Neural Network and analyzed the water level of the agricultural reservoir from CCTV images obtained from TOMS (Total Operation Management System) of the Korea Rural Community Corporation. As a result, the accuracy of water level detection was improved by excluding night and rainfall CCTV images and applying measures. For example, the error rate significantly decreased from 24.39 % to 1.43 % in the Bakseok reservoir. We believe that the utilization of CCTVs should be further improved when calculating the amount of water supply and establishing a supply plan according to the integrated water management policy.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.