• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.270-283
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• 2024

Purpose: Considering the rising frequency of earthquakes in Korea, it is crucial to revise the rainfall thresholds for landslide triggering following earthquake events. This study was conducted to provide scientific justification and preliminary data for adjusting rainfall thresholds for landslide early warnings after earthquakes through soil physical experiments. Method: The study analyzed the change in soil shear strength by direct shear tests on disturbed and undisturbed samples collected from cut slopes. Also, The study analyzed the soil strength parameters of remolded soil samples subjected to drying and wetting conditions, focusing on the relationship between the degree of saturation after submergence and the strength parameters. Result: Compaction water content variation in direct shear tests showed that higher water content and saturation in disturbed samples led to a significant decrease in cohesion (over 50%) and a reduction in shear resistance angle (1~2°). Additionally, during the ring shear tests, the shear strength was observed to gradually decrease once water was supplied to the shear plane. The maximum shear strength decreased by approximately 65-75%, while the residual shear strength decreased by approximately 53-60%. Conclusion: Seismic activity amplifies landslide risk during subsequent rainfall, necessitating proactive mitigation strategies in earthquake-prone areas. This research is anticipated to provide scientific justification and preliminary data for reducing the rainfall threshold for landslide initiation in earthquake-susceptible regions.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.106-116
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• 2024

Purpose: This paper conducts IoT and CCTV-based safety monitoring to analyze accidents and potential risks occurring at construction sites, and detect and analyze risks such as falls and collisions or abnormalities and to establish a system for early warning using devices like a walkie-talkie and chatbot service. Method: A safety management service model is presented through smart construction technology case studies at the construction site and review a relevant literature analysis. Result: According to 'Construction Accident Statistics,' in 2021, there were 26,888 casualties in the construction industry, accounting for 26.3% of all reported accidents. Fatalities in construction-related accidents amounted to 417 individuals, representing 50.5% of all industrial accident-related deaths. This study suggests implementing AI chatbot services for construction site safety management utilizing IoT-based health monitoring technologies in smart construction practices. Construction sites where stakeholders such as workers participate were demonstrated by implementing an artificial intelligence chatbot system by selecting major risk areas within the workplace, such as scaffolding processes, openings, and access to hazardous machinery. Conclusion: The possibility of commercialization was confirmed by receiving more than 90 points in the satisfaction survey of participating workers regarding the empirical results of the artificial intelligence chatbot service at construction sites.

• Journal of Korea Water Resources Association
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• v.56 no.2
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• pp.75-89
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• 2023

As the Mekong River basin is a nationally shared river, it is difficult to collect precipitation data, and the quantitative and qualitative quality of the data sets differs from country to country, which may increase the uncertainty of hydrological analysis results. Recently, with the development of remote sensing technology, it has become easier to obtain grid-based precipitation products(GPPs), and various hydrological analysis studies have been conducted in unmeasured or large watersheds using GPPs. In this study, rainfall-runoff simulation in the Mekong River basin was conducted using the SWAT model, which is a quasi-distribution model with three satellite GPPs (TRMM, GSMaP, PERSIANN-CDR) and two GPPs (APHRODITE, GPCC). Four water level stations, Luang Prabang, Pakse, Stung Treng, and Kratie, which are major outlets of the main Mekong River, were selected, and the parameters of the SWAT model were calibrated using APHRODITE as an observation value for the period from 2001 to 2011 and runoff simulations were verified for the period form 2012 to 2013. In addition, using the ConvAE, a convolutional neural network model, spatio-temporal correction of original satellite precipitation products was performed, and rainfall-runoff performances were compared before and after correction of satellite precipitation products. The original satellite precipitation products and GPCC showed a quantitatively under- or over-estimated or spatially very different pattern compared to APHPRODITE, whereas, in the case of satellite precipitation prodcuts corrected using ConvAE, spatial correlation was dramatically improved. In the case of runoff simulation, the runoff simulation results using the satellite precipitation products corrected by ConvAE for all the outlets have significantly improved accuracy than the runoff results using original satellite precipitation products. Therefore, the bias correction technique using the ConvAE technique presented in this study can be applied in various hydrological analysis for large watersheds where rain guage network is not dense.