• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3C
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• pp.167-178
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• 2008

Rainfall-induced slope failures were simulated by seepage and stability analyses for actual slopes of weathered soils. After undisturbed sampling and testing on a specimen of unsaturated conditions, a seepage analysis was performed under actual rainfall and it was found that the pore water pressure increased at the boundary of soil and rock layers. The safety factor of slope stability decreased below 1.0 and the failure of actual slope could be simulated. Under design rainfall intensity, the seepage analysis could not include the effects of the antecedent rainfall and the rainfall duration. Due to these limitations, the safety factor of slope stability resulted in above 1.0, since the hydraulic head of soil layers had not be affected significantly. In the analysis of another slope failure, the parameters of unsaturated conditions were evaluated using artificial neural network (ANN). In the analysis of seepage, the boundary of soil and rock was saturated sufficiently and then the safety factor could be calculated below 1.0. It was found that the failure of actual slope can be simulated by ANN-based estimation.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.191-195
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• 2007

Recently, landslides frequently happen at a natural slope during period of intensive rainfall. With rapidly increasing population of steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed in the world. In this study, a simple landslide prediction system that enables people to escape the endangered area is developed. The system is focused to debris flows which happen frequently during periods of intensive rainfall at steep slopes in Kangwondo. This system is based on the wireless sensor network that is composed of sensor nodes, gateway, and server system. Sensor nodes that are composed of sensing part and communication part are newly developed to detect sensitive ground movement. Sensing part is designed to measure tilt angle and acceleration accurately, and communication part is deployed with Bluetooth (IEEE 802.15. I) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of laboratory tests is performed at a small-scale earth slope supplying rainfall by artificial rainfall dropping device. It is found that sensing nodes installed at slope can detect the ground motion when the slope failure starts. It is expected that the landslide prediction system by wireless senor network can provide early warnings when landslides such as debris flow occurs, and can be applied to ubiquitous computing city (U-City) that is characterized by disaster free.

• Journal of Korean Society on Water Environment
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• v.24 no.1
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• pp.107-117
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• 2008

Infiltration facilities are effective instruments to mitigate flood and can increase base runoff in urban watersheds. In order to analyze effects of infiltration trenches physical model experiments were conducted. The physical model facility consists of two soil tanks, artificial rainfall generators, tensiometers, and piezometers. The experiment was conducted by nine times and each case differed in rainfall intensity, rainfall duration and the type of ground surface. Measured quantities in the experiments are as follows: surface runoff, subsurface runoff, trench pipe runoff, groundwater level, water content, etc. The following resulted from the model experiment: The volume of subsurface runoff at trench watershed was maximum 78.3% compared with rainfall. This value is bigger than that of ordinary rate of subsurface runoff, and shows a groundwater recharge effect of trench. The time of runoff passing through the trench became earlier and the volume of runoff became larger with the increase of inflow into the trench, while trench exfiltration into ground became relatively smaller. The results of this study presented above show that infiltration trenches are effective instruments to increase base runoff during dry periods.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Journal of Korea Water Resources Association
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• v.41 no.10
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• pp.995-1007
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• 2008

Studies on modeling the rainfall-runoff relationship which shows nonlinear trend strongly use artificial neural networks theory not only for the prediction but also for the characteristics analysis of the data used by pattern classification. For the pattern classification, the results from Self-Organizing Map (SOM) mention that the map size and array for the SOM training have significantly influenced on the SOM performance. Since there is no deterministic method or theoretical equation to determine the number of rows and columns for the map size, hexagonal array is generally used for the map array. Therefore, this study present a determination of the optimized map structure for the rainfall-runoff analysis in Naju station considering the map size and array simultaneously which can represent the classified characterization of rainfall-runoff relationship. The result showed that the map size of 20$\times$16 hexagonal array with 8-clustered patterns was selected as an appropriate map structure for rainfall-runoff analysis in Naju station.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.57-70
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• 2012

The aim of this study is to analyse the landslide susceptibility and the future hazard in Inje, Korea using probability rainfalls and artificial neural network (ANN) environment based on geographic information system (GIS). Data for rainfall probability, topography, and geology were collected, processed, and compiled in a spatial database using GIS. Deokjeok-ri that had experienced 694 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 1-day rainfall of 202 mm or 3-day cumulative rainfalls of 449 mm.

• Journal of the Korean GEO-environmental Society
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• v.15 no.4
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• pp.13-27
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• 2014

Catchments soil erosion, one of the most serious problems in the mountainous environment of the world, consists of a complex phenomenon involving the detachment of individual soil particles from the soil mass and their transport, storage and overland flow of rainfall, and infiltration. Sediment size distribution during erosion processes appear to depend on many factors such as rainfall characteristics, vegetation cover, hydraulic flow, soil properties and slope. This study involved laboratory flume experiments carried out under simulated rainfall in a 3.0 m long ${\times}$ 0.8 m wide ${\times}$ 0.7 m deep flume, set at $17^{\circ}$ slope. Five experimental cases, consisting of twelve experiments using three different sediments with two different rainfall conditions, are reported. The experiments consisted of detailed observations of particle size distribution of the out-flow sediment. Sediment water mixture out-flow hydrograph and sediment mass out-flow rate over time, moisture profiles at different points within the soil domain, and seepage outflow were also reported. Moisture profiles, seepage outflow, and movement of overland flow were clearly found to be controlled by water retention function and hydraulic function of the soil. The difference of grain size distribution of original soil bed and the out-flow sediment was found to be insignificant in the cases of uniform sediment used experiments. However, in the cases of non-uniform sediment used experiments the outflow sediment was found to be coarser than the original soil domain. The results indicated that the sediment transport mechanism is the combination of particle segregation, suspension/saltation and rolling along the travel distance.

• Journal of the Korean GEO-environmental Society
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• v.14 no.12
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• pp.5-12
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• 2013

In this study, weathered granite model tests were performed to investigate the variation of volumetric water content and matric suction by the adsorption and desorption processes of artificial rainfall. It has been compared with numerical analysis in unsaturated condition. As the results, the behaviors of volumetric water content and matric suction were distinguished by the seepage distance separated into higher, middle and lower area, and the drainage layer located at the bottom of the experimental device. In the adsorption process, the instantaneously large change of matric suction and water content were related to the increase of permeability in soil. However, in the desorption process, the change of matric suction and water content were gradually small because of the decrease of permeability. The volumetric water content and matric suction showed the difference according to the seepage distance, however the typical characteristic curves were made by the adsorption and desorption processes.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.398-398
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• 2015

To estimate and forecast runoff by using Aritifitial Neaural Networks model (ANNs). it has been studied in Thailand for the past 10 years. The model was developed in order to be conformed with the conditions in which the collected dataset is short and the amount of dataset is inadequate. Every year, the Northerpart of Thailand faces river overflow and flood inundation. The most important basin in this area is Yom basin. The purpose of this study is to forecast runoff at Y.14 gauge station (Si-Satchanalai district, Sukhothai province) for 3 days in advance. This station located at the upstream area of Yom River basin. Daily rainfall and daily runoff from Royal Irrigation Department and Meteorological Department during flood period 2000-2012 were used as input data. In order to check an accuracy of forecasting, forecasted runoff were compared with observed data by pursuing Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination ($R^2$). The result of the first day gets the highest accuracy and then decreased in day 2 and day 3, consequently. NSE and $R^2$ values for frist day of runoff forecasting is 0.76 and 0.776, respectively. On the second day, those values are 0.61 and 0.65, respectively. For the third day, the aforementioned valves are 0.51 and 0.52, respectively. The results confirmed that the ANNs model can be used when the range of collected dataset is short and insufficient. In conclusion, the ANNs model is suitable for applying during flood incident because it is easy to use and does not require numerous parameters for simulating.