• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.377-382
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• 2007

Mobile ad hoc network(MANET) is a kind of wireless network which has no infrastructure. Each component node of MANET can move freely and communicate based on wireless peer to peer mode. Because of its vulnerable routing protocols, MANET is exposed to many kinds of attacks. A sinkhole attack is one of the representative attacks in MANET caused by attempts to draw all network traffic to a sinkhole node. This paper focuses on the sinkhole problem on Dynamic Source Routing(DSR) protocol in MANET. To detect the sinkhole node, we extract several useful sinkhole indicators through analyzing the sinkhole problem, then propose an efficient detection method based on an incremental learning algorithm. The simulation results show that the proposed method is effective and reliable for detecting sinkhole intrusion.

• Journal of The Geomorphological Association of Korea
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• v.24 no.4
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• pp.75-89
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• 2017

Sinkholes are key karst landforms that primarily evolve through the dissolution of limestone, and it posing a significant threat to roads, buildings, and other man-made structures. This study aims to analyze the area susceptible to sinkhole development using GIS and to identify potential danger area from sinkholes. Eight sinkhole related factors (slope angle, distance to caves, distance to faults, bedrock lithology, soil depth, drainage class, distance to mines, and distance to traffic routes) were constructed as spatial databases with sinkhole inventory. Based on the spatial database, sinkhole susceptibility maps were produced using nearest neighbor distance and frequency ratio models. The maps were verified with prediction rate curve and area under curve. The result indicates that the nearest neighbor distance and frequency ratio models predicted 95.3% and 94.4% of possible sinkhole locations respectively. Furthermore, to identify potential sinkhole danger area, the susceptibility map was compared with population distribution and land use map. It has been found that very highly susceptible areas are along Osipcheon and southeast southwest part of Hajang-myeon and south part of Gagok-myeon of Samcheok-si. Among those areas, it has been identified that potential sinkhole danger areas are Gyo-dong, Seongnae-dong, Jeongna-dong, Namyang-dong and Dogye-eup. These results can be useful in the aspects of land use planning and hazard prevention and management.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.17-25
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• 2019

Accurate tracking of the sinkholes that are appearing frequently now is an important method of protecting human and property damage. Although many sinkhole detection systems have been proposed, it is still far from completely solved especially in-depth area. Furthermore, detection of sinkhole algorithms experienced the problem of unstable result that makes the system difficult to fire a warning in real-time. In this paper, we proposed a method of sinkhole tracking by deep learning and data association, that takes advantage of the recent development of CNN transfer learning. Our system consists of three main parts which are binary segmentation, sinkhole classification, and sinkhole tracking. The experiment results show that the sinkhole can be tracked in real-time on the dataset. These achievements have proven that the proposed system is able to apply to the practical application.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.341-349
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• 2020

Recently, interest in ground subsidence in urban areas has increased after a large sinkhole occurred near the high-story building area in Jamsil, Seoul, Korea, in 2014. If a massive sinkhole occurs in an urban area, it is crucial to assess its risk rapidly. Access to humans for on-site safety diagnosis may be difficult because of the additional risk of collapse in the disaster area. Generally, inspection using drones equipped with high-speed lidar sensors can be utilized. However, if the sinkhole is created vertically to a depth of 100 m, similar to the sinkhole in Guatemala, the drone cannot be applied because of the wireless communication limit and turbulence inside the sinkhole. In this study, a three-dimensional (3D) scanning system was fabricated and operated using a towed cable in a massive vertical sinkhole to a depth of 200 m. A high-speed lidar sensor was used to obtain a continuous cross-sectional shape at a certain depth. An inertial-measuring unit was applied to compensate for the error owing to the rotation and pendulum movement of the measuring unit. A reconstruction algorithm, including the compensation scheme, was developed. In a vertical hole with a depth of 180 m in the mining area, the fabricated system was applied to scan 0-165 m depth. The reconstructed shape was depicted in a 3D graph.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.3
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• pp.31-36
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• 2010

In this paper, We propose a transaction signing-based authentication scheme for protecting sinkhole attacks in wireless sensor networks. Sinkhole attack makes packets that flow network pass through attacker. So, Sinkhole attack can be extended to various kind of attacks such as denial of service attacks, selective delivery or data tamper etc. We analyze sinkhole attack methods in directed diffusion based wireless sensor networks. For the purpose of response to attack method, Transaction signing-based authentication scheme is proposed. This scheme can work for those sensor networks which use directed diffusion based wireless sensor networks. The validity of proposed scheme is provided by BAN logic.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.203-216
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• 2019

The nature of wireless transmission has made wireless sensor networks defenseless against various attacks. This paper presents warning message counter method (WMC) to detect blackhole attack, grayhole attack and sinkhole attack in wireless sensor networks. The objective of these attackers are, to draw the nearby network traffic by false routing information and disrupt the network operation through dropping all the received packets (blackhole attack), selectively dropping the received packets (grayhole and sinkhole attack) and modifying the content of the packet (sinkhole attack). We have also attempted light weighted symmetric key cryptography to find data modification by the sinkhole node. Simulation results shows that, WMC detects sinkhole attack, blackhole attack and grayhole attack with less false positive 8% and less false negative 6%.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.267-272
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• 2016

Wireless sensor networks (WSNs) are vulnerable to external intrusions due to the wireless communication characteristics and limited hardware resources. Thus, the attacker can cause sinkhole attack while intruding the network. INSENS is proposed for preventing the sinkhole attack. INSENS uses the three symmetric keys in order to prevent such sinkhole attacks. However, the sinkhole attack occurs again, even in the presence of INSENS, through the compromised node because INSENS does not consider the node being compromised. In this paper, we propose a method to counter the sinkhole attack by considering the compromised node, based on the neighboring nodes' information. The goals of the proposed method are i) network reliability improvement and ii) energy conservation through effective prevention of the sinkhole attack by detecting compromised nodes. The experimental results demonstrate that the proposed method can save up to, on average, 19.90% of energy while increasing up to, on average, 71.50%, the report reliability against internal sinkhole attacks in comparison to INSENS.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.9
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• pp.901-905
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• 2008

In this paper, we propose a detection scheme for sinkhole attacks in wireless sensor networks. Sinkhole attack makes packets that flow network pass through attacker. So, Sinkhole attack can be extended to various kind of attacks. We analyze sinkhole attack methods in the networks that use LQI based routing. For the purpose of response to each attack method, we propose methods to detect attacks. Our scheme can work for those sensor networks which use LQI based dynamic routing protocol. And we show the detection of sinkhole attack can be achieved by using a few detector nodes.

• Journal of the Speleological Society of Korea
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• v.34 no.35
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• pp.78-104
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• 1993

Analysis of one sinkhole, the Dodgeville sinkhole, developed in Ordovician dolostones in the Driftless Area of Wisconsin in the Upper Midwest'd Driftless Area reveals homogenous clayey sediment fills reflecting a range of dissolutional processes during the Quaternary or Pre-Quaternary. Granulometric analysis, graphical moments statistics, carbonate minerals, ana sand grain lithology were used to differentiate sinkhole sediment sources and modes of accumulation. Sediments in the dolostone sinkholes developed by dissolution. Sediments contain two major types of sediments : residual redish clay( autogenic sediments) and aeolian silt (allogenic sediments). The massive clay is generated from the weathered dolostone bedrocks as a in situ materials. The loessial silt is mostly derived from transportation of the surrounding surface materials, with some evidences of penetrated deposition. Unlike the collapsed sandstone sinkholes (Oh et al., 1993), dolostone sinkholes reveal homogenous, autogenic clay materials, and a geochemical composition indicative of in situ autogenic karstification. Dolostone sinkhole si1ts (26.9%) and sands (34.9%) are derived from weathered Plattevi1le-Galena dolostones, and contain high carbonate(37.5%), chert (57.2%) and lead ore (3%). Graphical moments statistics for sorting, skewness, and kurtosis indicate that sand grains from dolostones were derived entirely from local bedrock by in situ dissolution. Upper sinkhole sediments are pedagogically very young as carbonate is unleashed. Materials of the sinkhole sediment are definitely inherited from internal dolostones by dissolution and weathering, because not only a granulomatric comparison of dolostone and sandstone sediments demonstrates that they have heterogeneous paticle size distributions, but also 1ithologic analyses displays they differ completely.