• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.81-88
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• 2010

This paper describes a development of next generation information remote retrieval and warning system that enables the user to make slope information retrieval remotely for a rockfall and landslide disaster reduction from mobile environments. And this system will be able to warn with a real-time stability condition about the slope which circumference are contiguous in standard user location. Slope information which provides to the user, become the service which upgrades from depth deep information directness will be able to confirm in order from field with applies multimedia style information which is various. In order to retrieve slope information with the wire and wireless internet from the remote place, we used mobile PC carrying is simple. Also this system attached GPS receiver to mobile PC in order to confirm user location as a real-time from the electronic map from field. Specially this system user location divide the safety of the slope which within the area where are fixed in the center are representative with 'safe area', 'collapse area' and 'collapse forecast area' etc. And to indicate with the icon of each other different color simultaneously in the electronic map. With like that reason, this system which sees the user even while moving safety condition about circumferential slope from the electronic map is having the strong point will be able to grasp with a real-time in one eye. Also warning message leads at the case real-time when the collapse will occur in specific slope, to inform to the user. Therefore this system which sees will be able to reduce the disaster which is caused by in landslide a very big strong point and has.

• Journal of the Korean Geotechnical Society
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• v.32 no.6
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• pp.27-40
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• 2016

Conventional warning criteria for landslides due to rainfall in broad regions have limitations, because they did not have proper reflection of topography, forest physiognomy, and unsaturated soil properties, et al. This study suggested a new stability model for unsaturated slope analyses during rainfall, considering rainfall pattern, geomorphological characteristics (slope angle, soil depth), engineering properties of unsaturated soils, and tree surcharge and root reinforcement. Stability analysis not considering root reinforcement and tree surcharge tends to over-predict a factor of safety in unsaturated slopes. Developed slope stability model was used to build database on the factor of safety in unsaturated slopes during rainfall, and it was integrated with GIS to do quantitative risk analysis in landslide risk areas specified in Ulju. Landslide risk areas were located at downstream of the point with sudden drop in safety factor, as well as at regions with low safety factor during rainfall.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.1-8
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• 2021

In this paper, we propose the construction of an IOT that monitors foresters' physical abnormalities in real time, performs emergency measures, and provides alarms for natural disasters or heatstroke such as a nearby forest fire or landslide. Nodes provided to foresters include 6-axis sensors, temperature sensors, GPS, and LoRa, and transmit the measured data to the network server through the gateway using LoRa communication. The network server uses 6-axis sensor data to determine whether or not a forester has any signs of abnormal body, and performs emergency measures by tracking GPS location. After analyzing the temperature data, it provides an alarm when there is a possibility of heat stroke or when a forest fire or landslide occurs in the vicinity. In this paper, it was confirmed that the real-time detection of physical abnormalities of foresters and the establishment of disaster early warning IOT is possible by analyzing the data obtained by constructing a node and a gateway and constructing a network server.

• Journal of Korean Society of Forest Science
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• v.102 no.3
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• pp.463-466
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• 2013

We examined relationship between rainfall and triggering of shallow landslides in South Korea, based on hourly rainfall data for 478 shallow landslides during 1963-2012. Rainfall intensity(I) and duration(D) relationship was analyzed to obtain the I-D threshold for the initiation of a shallow landslide using the quantile regression analysis. The I-D threshold equation from in this study is: $I=9.64D^{-0.27}$($4{\leq}D{\leq}76$), where I and D are expressed in millimeters per hour and hours, respectively. In addition, rainfall criteria were proposed to predict the potential to cause landslides, based on values of I-D and cumulative rainfall derived from quantile regression analysis. Our findings may provide essential data and important evidences for the improvement of landslide warning and evacuation system.

• Structural Monitoring and Maintenance
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• v.7 no.1
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• pp.59-67
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• 2020

This study aims to develop the fuzzy risk assessment model of the debris flow to verify the accuracy of risk assessment in order to help related organizations reduce losses caused by landslides. In this study, actual cases of landslides that occurred are utilized as the database. The established models help us assess the occurrence of debris flows using computed indicators, and to verify the model errors. In addition, comparisons are made between the models to determine the best one to use in practical applications. The results prove that the risk assessment model systems are quite suitable for debris flow risk assessment. The reproduction consequences of highlight point discovery are shown in highlight guide coordinating toward discover steady and coordinating component focuses and effectively identified utilizing these two systems, by examining the variety in the distinguished highlights and the element coordinating.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.125-136
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• 2024

This study presents an automated Python algorithm for analyzing rainfall characteristics to establish critical rainfall thresholds as part of a landslide early warning system. Rainfall data were sourced from the Korea Meteorological Administration's Automatic Weather System (AWS) and the Korea Forest Service's Automatic Mountain Observation System (AMOS), while landslide data from 2020 to 2023 were gathered via the Life Safety Map. The algorithm involves three main steps: 1) processing rainfall data to correct inconsistencies and fill data gaps, 2) identifying the nearest observation station to each landslide location, and 3) conducting statistical analysis of rainfall characteristics. The analysis utilized power law and nonlinear regression, yielding an average R² of 0.45 for the relationships between rainfall intensity-duration, effective rainfall-duration, antecedent rainfall-duration, and maximum hourly rainfall-duration. The critical thresholds identified were 0.9-1.4 mm/hr for rainfall intensity, 68.5-132.5 mm for effective rainfall, 81.6-151.1 mm for antecedent rainfall, and 17.5-26.5 mm for maximum hourly rainfall. Validation using AUC-ROC analysis showed a low AUC value of 0.5, highlighting the limitations of using rainfall data alone to predict landslides. Additionally, the algorithm's speed performance evaluation revealed a total processing time of 30 minutes, further emphasizing the limitations of relying solely on rainfall data for disaster prediction. However, to mitigate loss of life and property damage due to disasters, it is crucial to establish criteria using quantitative and easily interpretable methods. Thus, the algorithm developed in this study is expected to contribute to reducing damage by providing a quantitative evaluation of critical rainfall thresholds that trigger landslides.

• The Journal of Engineering Geology
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• v.24 no.2
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• pp.191-199
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• 2014

We describe a method for detecting slope instability in flume tests using pore pressure and water content data in conjunction with a statistical control chart analysis. Specifically, we conducted univariate statistical analysis on x-MR control chart data (pore pressure and water content) collected at several points along the flume slope, which we separated into three parts: upper, middle, and lower. To assess our results in the context of landslide forecasting and warning systems, we applied control limit lines at $1{\sigma}$, $2{\sigma}$, and $3{\sigma}$ levels of uncertainty. In doing so, we observed that dispersion time varies depending on the control limit line used. Moreover, the detection of instabilities is highly dependent on the position and type of sensor. Our findings indicate that different characteristics of the data on various factors predict slope failure differently and these characteristics can be identified by univariate statistical analysis. Therefore, we suggest that a univariate statistical approach is an effective method for the early detection of slope instability.

• Structural Monitoring and Maintenance
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• v.6 no.2
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• pp.161-171
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• 2019

There have been a few checking frameworks intended to ensure and improve the nature of their regular habitat. The greater part of these frameworks are constrained in their capacities. In this paper, the insightful checking framework intended for debacle help and administrations has been exhibited. The ideal administrations, necessities and coming about plan proposition have been indicated. This has prompted a framework that depends fundamentally on ecological examination so as to offer consideration and security administrations to give the self-governance of indigenous habitats. In this sense, ecological acknowledgment is considered, where, in light of past work, novel commitments have been made to help include based and PC vision situations. This epic PC vision procedure utilized as notice framework for avalanche identification depends on changes in the normal landscape. The multi-criteria basic leadership strategy is used to incorporate slope data and the level of variety of the highlights. The reproduction consequences of highlight point discovery are shown in highlight guide coordinating toward discover steady and coordinating component focuses and effectively identified utilizing these two systems, by examining the variety in the distinguished highlights and the element coordinating.

• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.272-278
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• 2009

This study was conducted to develope calculation method of standard rainfall, which was used for predicting the outbreaking time of disaster by using Tank model, on warning and evacuation for soil sediment disaster. We investigate adeption possibility of developed method through comparing storage function method with Tank model. We calculated storage amount rainfall by storage function method and Tank model with 36 hillslope failures which have record on outbreaking time of disaster. The result in case of Sedimentary (quarternary period) showed that the difference of outbreaking time was 1.6 hour in case of tank model, but 3.2 hour in case of storage function method. In addition, the deviation of the peak storage were 7% in case of tank model, but 63% in case of storage function method. Total evacuation period was analyzed by using observed 5 years (1993-1997) rainfall data as well as each standard rainfalls which were determinated by two methods. The result showed that evacuation time by storage function method was about twice as many as that by tank model. Therefore, we concluded that calculation by tank model for predicting the outbreaking time of disaster was more useful and accurate than storage function method.

• Journal of Korean Society of Forest Science
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• v.105 no.2
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• pp.216-222
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• 2016

In relation to the impact of cumulative rainfall on landslides in accordance with the cumulative number of days, for the more than 100 mm rainfall, the 3 days cumulative rainfall experienced 64.9% of the total points, which is 986 points out of the 1520 points. The 5 days cumulative rainfall period experienced 60% of the total landslides, which is 846 points out of 1520 points analyzed. The 3 days or 5 days cumulative rainfall thus had a greater effect on landslides than the other days. In addition, for the 101-200 mm rainfall, more landslides occurred in the 10 days cumulative number of days, for the 201-300 mm, more landslides occurred in the 14 days cumulative number of days, whereas the 18 days cumulative number of days had more landslides for the 301-400 mm rainfall. Thus, it is imperative to take into consideration cumulative rainfall and the cumulative number of days of rainfall in the establishment of forecasting and warning systems for landslides, to minimize the damage caused to life and property by landslides.