• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.65-72
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• 2022

For sustainable development in the construction industry, blast furnace slag has been used as a substitute for cement in concrete. In contrast, ferronickel slag, which is the by-product generated during smelting to ferronickel used in the manufacturing of stainless steel and nickel alloys, has a limitation to use as a binder and an aggregate due to its expansive characteristics. Recently, stabilization technology of ferronickel slag has been improved and studies have been carried out to utilize ferronicke slag as fine aggregate in concrete. Therefore, in this study, basic mechanical properties of concrete used in ferronickel slag aggregate was evaluated. The compressive strength (24, 30, 40 MPa) and replacement rate of ferronickel slag aggregate (0, 10, 25, 50%) were considered as experimental variables. As a result of test, concrete replaced fine aggregate with 25% ferronickel slag aggregate showed superior performance in the compressive strength and flexural strength.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.1
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• pp.57-70
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• 2017

In this study, we evaluated daily root zone soil moisture dynamics and agricultural drought using a near-surface soil moisture data assimilation scheme with Soil Moisture Active & Passive (SMAP, $3km{\times}3km$) soil moisture footprints under different hydro-climate conditions. Satellite-based LANDSAT and MODIS image footprints were converted to spatially-distributed soil moisture estimates based on the regression model, and the converted soil moisture distributions were used for assessing uncertainties and applicability of SMAP data at fields. In order to overcome drawbacks of the discontinuity of SMAP data at the spatio-temporal scales, the data assimilation was applied to SMAP for estimating daily soil moisture dynamics at the spatial domain. Then, daily soil moisture values were used to estimate weekly agricultural drought based on the Soil Moisture Deficit Index (SMDI). The Yongdam-dam and Soyan river-dam watersheds were selected for validating our proposed approach. As a results, the MODIS/SMAP soil moisture values were relatively overestimated compared to those of the TDR-based measurements and LANDSAT data. When we applied the data assimilation scheme to SMAP, uncertainties were highly reduced compared to the TDR measurements. The estimated daily root zone soil moisture dynamics and agricultural drought from SMAP showed the variability at the sptio-temporal scales indicating that soil moisture values are influenced by not only the precipitation, but also the land surface characteristics. These findings can be useful for establishing efficient water management plans in hydrology and agricultural drought.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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• pp.93-103
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• 2020

In this study, we estimated soil moisture values using the Deep Neural Network(DNN) scheme at the mountainous regions. In order to test the sensitive analysis of DNN scheme, we collected the measured(at the soil depths of 10 cm and 30 cm) soil moisture and DNN input(weather and land surface) data at the Pyeongchang-gun(relatively flat) and Geochang-gun(steep slope) sites. Our findings indicated that the soil moisture estimates were sensitive to the weather variables(5 days-averaged rainfall, 5 days precedent rainfall, accumlated rainfall) and DEM. These findings showed that the DEM and weather variables play the key role in the processes of soil water flow at the mountainous regions. We estimated the soil moisture values at the soil depths of 10 cm and 30 cm using DNN at two study sites under different climate-landsurface conditions. The estimated soil moisture(R: 0.890 and RMSE: 0.041) values at the soil depth of 10 cm were comparable with the measured data in Pyeongchang-gun site while the soil moisture estimates(R: 0.843 and RMSE: 0.048) at the soil depth of 30 cm were relatively biased. The DNN-based soil moisture values(R: 0.997/0.995 and RMSE: 0.014/0.006) at the soil depth of 10 cm/30 cm matched well with the measured data in Geochang-gun site. Although uncertainties exist in the results, our findings indicated that the DNN-based soil moisture estimation scheme demonstrated the good performance in estimating soil moisture values using weather and land surface information at the monitoring sites. Our proposed scheme can be useful for efficient land surface management in various areas such as agriculture, forest hydrology, etc.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.6
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• pp.29-37
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• 2017

In this study, we analyzed the impacts of land surface characteristics on spatially and temporally distributed soil moisture values at the Yongdam and Soyang-river dam watersheds in 2014 and 2015. The soil moisture, NDVI (Normalized Difference Vegetation Index) and temperature values at the spatio-temporal scales were estimated using satellite-based MODIS (MODerate Resolution Imaging Spectroradiometer) products. Then the Pearson correlations between soil moisture and land surface characteristics (NDVI, temperature and DEM-digital elevation model) were estimated and analyzed, respectively. Overall, the monthly soil moisture values at the time step were highly influenced by the precipitation amounts. Also, the results showed that the soil moisture has the strong correlation with DEM while the temperature was inversely correlated with the soil moisture. However the monthly correlations between NDVI and soil moisture were highly varied along the time step. These findings indicated that water loss near the land surface are highly occurred by soil and plant activities as evapotranspiration and infiltration during the no/less precipitation period. But the high precipitation amounts reduce the impacts of land surface characteristics because of saturated condition of land surface. Thus these results demonstrated that soil moisture values are highly correlated with land surface characteristics. Our findings can be useful for water resources/environmental management, agricultural drought, etc.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.5
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• pp.127-136
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• 2017

The object of this study was to assess availability of meteorological drought index for agricultural dorught estimation in ungauged area of agricultural drought parameters which are reservoir water level and soil moisture. The IADI (Integrated Agricultural Drought Index) and the SPI (Standard Precipitation Index), which are the criteria for determining agricultural drought and meteorological drought, were calculated and compared. For this purpose, the droughts that occurred in the Baeksan reservoir in Gimje and the Edong reservoir in Suwon were evaluated by using the IADI and SPI drought indecies. In addition, we compared and analyzed the depth of drought based on the two drought indices. Evaluations derived form the IADI and SPI showed that the standard precipitation index tended to indicate the occurrence of drought earlier than the integrated agricultural drought index. However, the integrated agricultural drought index was better than the standard precipitation index at evaluating the severity of drought during the period of irrigation. The relationship between these two drought indices seems to be useful for decision making in the case of drought, and it is considered that more studies are needed to examine the applicability of these drought indexes.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.31-43
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• 2022

In this study, we estimated the monthly FDSI (Flash Drought Stress Index) for assessing flash drought on South Korea using AMSR2(Advanced Microwave Scanning Radiometer 2) satellite-based soil moisture footprints. We collected the AMSR2 soil moisture and climate-land surface data from April to November 2018 for analyzing the monthly FDSI values. We confirmed that the FDSI values were high at the regions with the high temperature/evapotranspiration while the precipitation is relatively low. Especially, the regions which satisfied an onset of flash drought (FDSI≧0.71) were increased from June. Then, the most of regions suffered by flash drought during the periods (July to August) with the high temperature and evapotranspiration. Additionally, the impacts of landuse and slope degree were evaluated on the monthly FDSI changes. The forest regions that have the steep slope degree showed the relatively higher FDSI values than the others. Thus, our results indicated that the the slope degree has the relatively higher impact on the onset and increasing of flash drought compared to the others.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.203-209
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• 2023

One major advantage of ground penetrating radar (GPR) over other field test methods is its ability to obtain subsurface images of roads in an efficient and non-intrusive manner. Not only can the strata of pavement structure be retrieved from the GPR scan images, but also various irregularities, such as cracks and internal cavities. This article introduces a deep learning-based approach, focusing on detecting subsurface cracks by recognizing their distinctive hyperbolic signatures in the GPR scan images. Given the limited road sections that contain target features, two data augmentation methods, i.e., feature insertion and generation, are implemented, resulting in 9,174 GPR scan images. One of the most popular real-time object detection models, You Only Learn One Representation (YOLOR), is trained for detecting the target features for two types of subsurface cracks: bottom cracks and full cracks from the GPR scan images. The former represents partial cracks initiated from the bottom of the asphalt layer or base layers, while the latter includes extended cracks that penetrate these layers. Our experiments show the test average precisions of 0.769, 0.803 and 0.735 for all cracks, bottom cracks, and full cracks, respectively. This demonstrates the practicality of deep learning-based methods in detecting subsurface cracks from GPR scan images.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.6
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• pp.101-112
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• 2022

Extreme precipitation events have recently become a leading cause of disasters. Thus, investigating the variability and trends of extreme precipitation is crucial to mitigate the increasing impact of such events. Spatial distribution and temporal trends in annual precipitation and four extreme precipitation indices of duration (CWD), frequency (R10 mm), intensity (Rx1day), and percentile-based threshold (R95pTOT) were analyzed using the daily precipitation data of 10 observation stations in Chungcheong province during 1974-2020. The precipitation at all observation stations, except the Boryeong station, showed nonsignificant increasing trends at 95% confidence level (CL) and increasing magnitudes from the west to east regions. The high variability in mean annual precipitation was more pronounced around the northeast and northwest regions. Similarly, there were moderate to high patterns in extreme precipitation indices around the northeast region. However, the precipitation indices of duration and frequency consistently increased from the west to east regions, while those of intensity and percentile-based threshold increased from the south to east regions. Nonsignificant increasing trends dominated in CWD, R10 mm, and Rx1day at all stations, except for R10 mm at Boeun station and Rx1day at Cheongju and Jecheon stations, which showed a significantly increasing trend. The spatial distribution of trend magnitude shows that R10 mm increased from the west to east regions. Furthermore, variations in precipitation were very strongly correlated (99% CL) with R10 mm, Rx1day, and R95pTOT at all stations, except with wR10 mm at Cheongju station, which was strongly correlated with a 95% CL.

• Proceedings of the Korean Environmental Sciences Society Conference
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• 2020.10a
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• pp.222-222
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• 2020

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.955-967
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• 2023

Climate change is expected to amplify the future flooding risks in rural areas which could have devastating implications for the sustainability of the agricultural sector and food security in South Korea. In this study, spatially disaggregated and statistically bias-corrected outputs from three global circulation models (GCMs) archived in the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6) were used to project the future climate by 2100 under medium and extreme scenarios. A hydrological model was developed to simulate the flood phenomena at the Shindae experimental site located in the Chungcheongbuk Province, South Korea. Hourly rainfall, inundation depth, and discharge data collected during the two extreme events that occurred in 2021 and 2022 were used to calibrate and validate the hydrological model. Probability analysis of extreme rainfall data suggested a higher likelihood of intense and unprecedented extreme rainfall events, which would be particularly notable during 2051-2100. Consequently, the flooded area under an inundation depth of >700 mm increased by 13-36%, 54-74%, and 71-90% during 2015-2030, 2031-2050, and 2051-2100, respectively. Severe flooding probability was notably higher under extreme CMIP6 scenarios than under their CMIP5 counterparts.