Saro Lee

Korea Institute of Geoscience and Mineral Resources | KIGAM · Geological Mapping Department

Radon is a naturally occurring radioactive gas found in many terrestrial materials. Due to the potential health risks linked to persistent exposure to high radon concentrations, it is essential to investigate indoor radon accumulation. This study generated indoor radon index maps for Chungcheongbuk-do, South Korea, selected factors with frequency r...

Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and without subsidence in the period from 2018 to 2020. In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR...

Susceptibility mapping is an important component of natural hazard risk assessment and management. Susceptibility maps for floods and landslides, which are particularly damaging to human life and property, can provide a comprehensive understanding of risk areas and factors related to flood and landslide susceptibility. To create a global flood and...

To address the growing global concern regarding increased wildfire occurrences and their widespread socio-ecological impacts, cost-effective and practical approaches must be urgently identified to accurately predict the probability of wildfire incidents. The objective of this study was to develop deep learning models to estimate the likelihood of w...

Managing natural hazards such as land subsidence (LS) is important because they cause large economic and human loss. LS has become a significant challenge in South Korea due to its many abandoned coal mines. Therefore, preparing LS zoning maps is vital to controlling damage caused by LS. In this study, genetic algorithm (GA) and binary whale optimi...

Locations prone to landslides must be identified and mapped to prevent landslide-related damage and casualties. Machine learning approaches have proven effective for such tasks and have thus been widely applied. However, owing to the rapid development of data-driven approaches, deep learning methods that can exhibit enhanced prediction accuracies h...

Landslides are among the most devastating natural hazards, severely impacting human lives and damaging property and infrastructure. Landslide susceptibility maps, which help to identify which regions in a given area are at greater risk of a landslide occurring, are a key tool for effective mitigation. Research in this field has grown immensely, ran...

Water scarcity is a severe problem in Tunisia, particularly in the northern region crossed by the Medjerda River, where groundwater is a conjoint water resource that is increasingly exploited. The aim of this study is to delineate the groundwater potential zones (GWPZs) in the Lower Valley of the Medjerda basin by using single benchmark machine lea...

Prolonged inhalation of indoor radon and its progenies lead to severe health problems for housing occupants; therefore, housing developments in radon-prone areas are of great concern to local municipalities. Areas with high potential for radon exposure must be identified to implement cost-effective radon mitigation plans successfully or to prevent...

This study aims at optimizing the support vector regression (SVR) model using four metaheuristic methods, Harris hawks optimization (HHO), particle swarm optimization (PSO), gray wolf optimizer (GWO), and bat algorithm (BA). The intent is to create a reliable flood susceptibility map (FSM). In this regard, a flood inventory map for 617 flood locati...

Landslides are one of the most destructive natural phenomena in the world, which occur mostly in mountainous areas and cause damage to the economic sectors, agricultural lands, residential areas and infrastructures of any country, and also threaten the lives and property of human beings. Therefore, landslide susceptibility mapping (LSM) can play a...

Schools as social bases and children’s centers are among the most vulnerable areas to flooding. Flood susceptibility mapping is very important for flood preparedness and adopting preventive plans for reducing the school vulnerability to flooding. To achieve this, there is a need for the models that can be used in vast areas with high predictive acc...

Land subsidence (LS), which mainly results from poor watershed management, is a complex and non-linear phenomenon. In the present study, LS at a country-wide assessment of Iran was mapped by using several geo-environmental conditioning factors (namely, altitude, slope degree and aspect, plan and profile curvature, distance from a river, road, or fa...

Although the growing number of synthetic aperture radar (SAR) satellites has increased their application in flood-extent mapping, predictive models for the analysis of flood dynamics that are independent of sensor characteristics must be developed to fully extract information from SAR images for flood mitigation. This study aimed to develop hybrid...

The dramatic increase in flood incidents as a significant threat to human life and property, environment, and infrastructure indicates the necessity of mapping spatial distribution of flood susceptible areas to reduce destructive effects of flooding. During the last decade, the integration of the geographic information system (GIS) with the remote...

Landslides are geological hazards that can have severe impacts, threatening both the people and the local environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting landslides and mitigating landslide-associated damage in areas prone to these events. This study aims to investigate the combinati...

Landslides are a geological hazard that can pose a serious threat to human health and the environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting and mitigating landslides. This study aimed to investigate the application of deep learning algorithms based on convolutional neural networks (CNN...

Study region The study area was the Anseong-si area that located in the southernmost part of Gyeonggi-do Province at 127°19′ E, 36°82′ N. Anseong has a transitional climate between that the north and the south regions. Its climate is characterized by the geographical conditions of forming expansive plains that stretch from the Charyeong Range. The...

In addition to a total of 20 types of topography/moisture analysis maps that have already been built using a digital topographic model (DEM), in this study, a total of 32 types including 26 types of topographic analysis diagrams and 6 types of hydrological analysis diagrams were performed through various topography/hydraulic analysis. Additional sp...

Radon potential mapping is challenging due to the limited availability of information. In this study, a new modeling process using deep learning models based on convolution neural network (CNN), long short-term memory (LSTM), and recurrent neural network (RNN) is presented to predict radon potential in the northwestern part of Gangwon Province, Sou...

Landslides are one of the most frequent and important natural disasters in the world. The purpose of this study is to evaluate the landslide susceptibility in Zhenping County using a hybrid of support vector regression (SVR) with grey wolf optimizer (GWO) and firefly algorithm (FA) by frequency ratio (FR) preprocessed. Therefore, a landslide invent...

Schools as social bases and children’s centers are among the most vulnerable areas to flooding. Flood risk mapping (FRM) is very important for flood preparedness and adopting preventive plans for reducing the school vulnerability to flooding. To achieve this, there is a need for the models that can be used in vast areas with high predictive accurac...

The robustness of landslide prediction models has become a major focus of researchers worldwide. We developed two novel hybrid predictive models that combine the self-organizing, deep-learning group method of data handling (GMDH) with two swarm intelligence optimization algorithms, i.e., cuckoo search algorithm (CSA) and whale optimization algorith...

Task allocation under uncertain conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent-based simulation to investigate task allocation considering appropriate spatial strategies to manage uncertainty in urban search and rescue (USAR) operations. The proposed method is based on the...

Landslide susceptibility (LS) mapping is an essential tool for landslide risk assessment. This study aimed to provide a new approach with better performance for landslide mapping and adopting readily available variables. In addition, it investigates the capability of a state-of-the-art model developed using the group method of data handling (GMDH)...

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the freq...

Snow avalanches impose a considerable threat to infrastructure and human safety in snow bound mountain areas. Nevertheless, the spatial prediction of snow avalanches has received little research attention in many vulnerable parts of the world, particularly in developing countries. The present study investigates the applicability of a stand-alone co...

Continuous generation of radon gas by soil and rocks rich in components of the uranium chain, along with prolonged inhalation of radon progeny in enclosed spaces, can lead to severe respiratory diseases. Detection of radon-prone areas and acquisition of detailed knowledge regarding relationships between indoor radon variations and geogenic factors...

Landslides are one of the most destructive natural phenomena in the world, which occur mostly in mountainous areas and cause damage to the economic sectors, agricultural lands, residential areas and infrastructures of any country, and also threaten the lives and property of human beings. Therefore, landslide susceptibility mapping (LSM) can play a...

Study region The present study has been carried out in the Tabriz River basin (5397 km2) in north-western Iran. Elevations vary from 1274 to 3678 m above sea level, and slope angles range from 0 to 150.9 %. The average annual minimum and maximum temperatures are 2 °C and 12 °C, respectively. The average annual rainfall ranges from 243 to 641 mm, an...

In this study, the correlation between specific capacity (SPC) and transmissivity (T) values of groundwater and various geological, topographical, soil, clinical, and forest-related factors was calculated using a probability technique-frequency ratio. Then, the groundwater potential maps were created using the frequency ratio model with a resolutio...

Flood-susceptibility mapping is an important component of flood risk management to control the effects of natural hazards and prevention of injury. We used a remote-sensing and geographic information system (GIS) platform and a machine-learning model to develop a flood susceptibility map of Kangsabati River Basin, India where flash flood is common...

Identification of flood-prone sites in urban environments is necessary, but there is insufficient hydraulic information and time series data on surface runoff. To date, several attempts have been made to apply deep-learning models for flood hazard mapping in urban areas. This study evaluated the capability of convolutional neural network (NNETC) an...

The Jangsu-gun area in the central Southwestern South Korea consists of a well-preserved Middle Paleoproterozoic gneissic basement, as well as the Late Triassic and Early Jurassic granitic rocks. Here, we present the detailed zircon U-Pb age data and whole-rock chemical compositions, including radioactive elements (e.g., U and Th) and activity conc...

As computer and space technologies have developed, geoscience information systems (GIS) and remote sensing (RS) techniques have also been rapidly growing [...]

The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and...

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identifi...

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model (DEM) data. The unique terrain characteristics of a particular landscape are derived from DEM, which are responsible for initiation and development of ephemeral gullies. As the topographic features of an area significantly influences on t...

Although the prediction of debris flow-prone areas represents a key step towards reducing damages, modeling debris flow susceptibility is complicated. In addition, the role of debris flow causal drivers in forested mountain landscapes are still poorly understood. To gain a holistic view of the causes of debris flows in the Umyeonsan, Seoul, South K...

The availability of groundwater is of concern. The demand for groundwater in Korea increased by more than 100% during the period 1994–2014. This problem will increase with population growth. Thus, a reliable groundwater analysis model for regional scale studies is needed. This study used the geographical information system (GIS) data and machine le...

The study has been carried out with an objective to prepare Siberian roe deer habitat potential maps in South Korea based on three GIS-based models including frequency ratio (FR) as a bivariate statistical approach as well as convolutional neural network (CNN) and long short-term memory (LSTM) as machine learning algorithms. According to field obse...

Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and to change land-use planning. This work is exploring and researching the potential of a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution for spatial mapping of the suscept...

Flood probability maps are essential for a range of applications, including land use planning and developing mitigation strategies and early warning systems. This study describes the potential application of two architectures of deep learning neural networks, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), for spatia...

Acute hydrological natural hazards such as floods not only affect the lives and properties of people but also causes severe damage to the critical infrastructures, which needs to be functioning even in substandard situations. Therefore, it is significant to predict the flood-prone areas for understanding how critical infrastructures are exposed to...

Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T., 2020. Special Issue on “Advances in Geospatial Research of Coastal Environments”. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. vi-xiii. Coconut Creek (Florida), ISSN 0749-0208....

Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better manageme...

In recent years, the incidence of localized heavy rainfall has increased as abnormal weather events occur more frequently. In densely populated urban areas, this type of heavy rain can cause extreme landslide damage, so that it is necessary to estimate and analyze the susceptibility of future landslides. In this regard, deep learning (DL) methodolo...

Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the adverse effect of land degradation and diminishes this type of harm...

Task allocation in uncertainty conditions is a key problem for agents attempting to achieve harmony in disaster environments. This paper presents an agent-based simulation to investigate tasks allocation through the consideration of appropriate spatial strategies to deal with uncertainty in urban search and rescue (USAR) operation. The proposed met...

Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The key objective of this research is to develop landslide susceptibility maps for the Taleghan basin of...

The extreme form of land degradation through different forms of erosion is one of the major problems in subtropical monsoon dominated region. The formation and development of gullies is the dominant form or active process of erosion in this region. So, identification of erosion prone regions is necessary for escaping this type of situation and main...

Landslides can cause considerable loss of life and damage to property, and are among the most frequent natural hazards worldwide. One of the most fundamental and simple approaches to reduce damage is to prepare a landslide hazard map. Accurate prediction of areas highly prone to future landslides is important for decision-making. In the present stu...

Iran experiences frequent destructive floods with significant socioeconomic consequences. Quantifying the accurate impacts of such natural hazards, however, is a complicated task. The present study uses a deep learning convolutional neural networks (CNN) algorithm, which is among the newer and most powerful algorithms in big data sets, to develop a...

The extreme form of land degradation caused by the formation of gullies is a major challenge for the sustainability of land resources. This problem is more vulnerable in the arid and semi-arid environment and associated damage to agriculture and allied economic activities. Appropriate modeling of such erosion is therefore needed with optimum accura...

Floods are among the deadliest natural hazards for humans and the environment. Identifying the most flood-susceptible areas is a fundamental step in the development of flood mitigation strategies and for reducing flood damage. There is an ongoing global debate regarding the most suitable model for flood-susceptibility modeling and predictions. Ther...

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared...

As threats of landslide hazards have become gradually more severe in recent decades, studies on landslide prevention and mitigation have attracted widespread attention in relevant domains. A hot research topic has been the ability to predict landslide susceptibility, which can be used to design schemes of land exploitation and urban development in...

Landslides are natural and sometimes quasi-natural hazards that are destructive to natural resources and cause loss of human life every year. Hence, preparing susceptibility maps for landslide monitoring is essential to minimizing their negative effects. The main aim of the current research was to develop landslide susceptibility maps for Icheon To...

Groundwater (GW) resources provide a large share of the world’s water demand for various sections such as agriculture, industry, and drinking water. Particularly in the arid and semi-arid regions, with surface water scarcity and high evaporation, GW is a valuable commodity. Yet, GW data are often incomplete or nonexistent. Therefore, it is a challe...

Freshwater shortages have become much more common globally in recent years. Water resources that are naturally available beneath the surface are capable of reversing this condition. Spatial modeling of groundwater distribution is an important undertaking that would aid in subsequent conservation and management of groundwater resources. In this stud...

We generated high-quality shallow landslide susceptibility maps for Bijar County, Kurdistan Province, Iran, using Random Forest (RAF), an ensemble computational intelligence method and three meta classifiers-Bagging (BA, BA-RAF), Random Subspace (RS, RS-RAF), and Rotation Forest (RF, RF-RAF). Modeling and validation were done on 111 shallow landsli...

Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun...

The Special Issue on “Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations” is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as...