GEO DATA

Review Paper

Global Geospatial Data for Flood and Landslide Susceptibility Mapping: Saro Lee, Rezaie Fatemeh; GEO DATA. 2023;5(4):380-393. Published online December 28, 2023; DOI: https://doi.org/10.22761/GD.2023.0058

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Abstract PDF: 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 landslide susceptibility map, global geospatial data for 37,984 landslide and 6,682 flood locations, as well as 11 selected environmental factors were used to construct a geographic information system database. The 11 environmental factors found to influence flood and landslide occurrence were rainfall, slope, terrain position index, plane curvature, terrain wetness index, distance from rivers, land use, soil texture, soil moisture, geology, and temperature. These data were then used directly to create a global flood and landslide susceptibility map.

Original Paper

Construction of Exploration Data for Greenhouse Gas Geologic Storage: Focusing on Geological Cross-section Data: Bokyun Ko, Sungjae Park, Saro Lee; GEO DATA. 2023;5(3):222-229. Published online September 26, 2023; DOI: https://doi.org/10.22761/GD.2023.0023

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Abstract PDF: In this study, the most basic data, underground geological structure data, that is, geological cross-section data, were established to select a candidate site for underground storage of greenhouse gases based on AI. As a target area, the Gyeongsang Basin, where a large amount of sedimentary rocks are distributed, was selected as the greenhouse gas can be stored most effectively in sedimentary rocks. To this end, the acquisition and edit step, the refinement step, and the labeling step were carried out in the order of raw data collection, source data and labeling data construction to construct the geological cross-section data. This data can be downloaded through the AI hub site (https://aihub.or.kr/aihubdata/data/view.do?curr Menu=115&topMenu=100&aihubDataSe=realm&dataSetSn=71390) operated by the Korea Institute for Intelligent Information Society Promotion.

Review

Data used for GIS-based Flood Susceptibility Mapping: Saro Lee, Fatemeh Rezaie; GEO DATA. 2022;4(1):1-15. Published online March 31, 2022; DOI: https://doi.org/10.22761/DJ2022.4.1.001

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7 Citations

Abstract PDF

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 sensing data provide efficient means to generate a more reliable and precise flood susceptibility map. The present study contains a review of 200 articles on the application of GIS-based methods in indicating flood vulnerable areas. The papers were reviewed in terms of influential variables, study area, and the number of articles published in the last 10 years. The review shows that the number of studies has increased since 2012. The total study areas covered 39 countries that were mostly located in Asia where the major developments and infrastructures have been constructed in the floodplains. The most common study areas was Iran (44 articles, 22%), followed by India (26 articles, 13%), China (26 articles, 11%), and Vietnam (15 articles, 7.5%). More than 90 variables were considered to map flood susceptible areas that the top 5 widely used flood conditioning factor are slope (98% of total articles), followed by elevation (92% of total articles), land use/land cover (79.5% of total articles), distance to the river (76.5% of total articles), and rainfall (73% of total articles). The review implies that many natural and anthropogenic factors affect flooding and the combination of both groups of factors is necessary to accurately detect and map flood-prone parts of the study area.

Citations

Citations to this article as recorded by

Assessment of flood susceptibility in Cachar district of Assam, India using GIS-based multi-criteria decision-making and analytical hierarchy process
Preeti Barsha Borah, Arpana Handique, Chandra Kumar Dutta, Diram Bori, Shukla Acharjee, Lanusashi Longkumer
Natural Hazards.2025; 121(6): 7625. CrossRef
Mapping flood resilience: a comprehensive geospatial insight into regional vulnerabilities
Tauheed Ullah Khan, Ghulam Nabi, Sana Ullah, Ali Akbar, James Kehinde Omifolaji, Jahangir Khan Achakzai, Arshad Iqbal
Frontiers in Water.2025;[Epub] CrossRef
Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard
Esaie Dufitimana, Paterne Gahungu, Ernest Uwayezu, Emmy Mugisha, Jean Pierre Bizimana
ISPRS International Journal of Geo-Information.2025; 14(4): 161. CrossRef
Evaluating flood hazard risks using multicriteria analytical hierarchy process and rice farmers’ risk perceptions in Bharathapuzha River Basin, Kerala, South India
Dhanya Punnoli, Jayarajan Kunnampalli, Sreeraj Punnoli, Suresh Selvaraj
Environmental Science and Pollution Research.2025;[Epub] CrossRef
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Kemal Ersayın, Mustafa Yaş
Black Sea Journal of Engineering and Science.2024; 7(4): 683. CrossRef
Geo-Spatial Based Cyclone Shelter Suitability Assessment Using Analytical Hierarchy Process (AHP) in the Coastal Region of Bangladesh
Irteja Hasan, Md. Omar Faruk, Zarin Tasnim Katha, Md. Osman Goni, Md Shafiqul Islam, Tapas Ranjan Chakraborty, Sheikh Fahim Faysal Sowrav, Md Shakhawat Hossain
Heliyon.2024; : e39831. CrossRef
A systematic flood risk assessment of Bloemfontein Watershed, South Africa
Zachariah H. Mshelia, Johanes A. Belle
Geomatics, Natural Hazards and Risk.2024;[Epub] CrossRef

Articles

Construction of Topographic/Hydrologic Data using DEM and its Service 2: Saro Lee, Minju Kim; GEO DATA. 2021;3(4):1-10. Published online December 31, 2021; DOI: https://doi.org/10.22761/DJ2021.3.4.001

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Abstract PDF: 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 species were built. As with the existing established data, the construction range is the whole of South Korea, the resolution is 30 m, and the coordinate system is UTM-K coordinates. The constructed data format is provided in three formats: ASCII, ARCGIS GRID, and GEOTIFF. All these topographic / hydrological analysis data can be downloaded free of charge from the Environmental Big Data Platform (www.bigdata-environment.kr).

Statistical Data of Mineral Commodity: Sunjin Lee, Yujeong Kim, Saro Lee; GEO DATA. 2021;3(3):16-34. Published online September 30, 2021; DOI: https://doi.org/10.22761/DJ2021.3.3.003

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Abstract PDF: The advanced countries, including the United States of America, Japan, the United Kingdom, etc., struggle to secure mineral resources for their development of economy and industry themselves. We, the Republic of Korea need to respond to come up with an effective counterplan for the resource supply and demand accordingly. The Korea Institute of Geological Resources (KIGAM) is a state-designated approval statistic institution and has been building the mineral commodity statistic data of the minerals, officially defined by the mining act of the Korean government since 1986. The statistical data of mineral commodity consist of the sales volume, distribution, and production of mines, and the labor conditions for operation, etc., based on the type of ore and the production of the region, month and mine. KIGAM published the various statistical data containing the production, import, export of the mineral commodity in various forms, including annual and monthly reports and by English. This mineral commodity/mining statistics data can be viewed and downloaded from Korean Statistical Information Services(mici.kigam.re.kr), (www.kosis.kr) by Statistic Korea, Mineral Resources Statistics Portal and also bigdata environment-platform(www.bigdataenvirnment.kr).

Digital Coal Geological Map: Saro Lee; GEO DATA. 2021;3(3):1-7. Published online September 30, 2021; DOI: https://doi.org/10.22761/DJ2021.3.3.001

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Abstract PDF: From 1974 to 1994, the Korea Institute of Geoscience and Mineral Resources (KIGAM) systematically prepared and published relatively precise coal cell geology maps of 1:10,000 or 1:25,000 scale for major coal fields across the country. Such a coal cell geology map includes information about the coal seams as well as the geology of the coal field area, so it can be used as an important basic data for coal development. In this paper, the current state of the coal geology map, which was digitized into a spatial DB using GIS, was introduced. This digital coal geological map can be downloaded free of charge from the Geo-Big Data Open Platform (data.kigam.re.kr) and the Environmental Big Data Platform (www.bigdata-environment.kr).

Mapping of Groundwater Productivity in Entire South Korea using Probabilistic Model: Saro Lee, Panahi Mahdi; GEO DATA. 2021;3(2):25-31. Published online July 21, 2021; DOI: https://doi.org/10.22761/DJ2021.3.2.004

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Abstract PDF: 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 resolution of 30 m for the entire South Korea. The maps were validated using the quantitative ROC (Receiver Operating Characteristic)-AUC (Area Under the Curve) method and the results showed the accuracy of 83.52% for specific capacity and 81.92% for Transmissivity. The groundwater potential maps can be used as basic data of groundwater development and downloaded free of charge from the environmental big data platform (www.bigdata-environment.kr).

Construction of Topographic/Hydrologic Data using DEM and Its Service: Saro Lee, Minji Kim; GEO DATA. 2020;2(2):36-44. Published online December 30, 2020; DOI: https://doi.org/10.22761/DJ2020.2.2.006

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124 Download
4 Citations

Abstract PDF

In this study, 10 types of terrain analysis maps and 10 types of hydrology analysis maps were constructed through various topographic/hydrological analysis using a digital topographic model (DEM). The construction range is for the entire South Korea, the resolution is 30m, and the coordinate system is UTM-K coordinates. The constructed data format is provided in three formats: ASCII, ARCGIS GRID, and GEOTIFF. In addition, all data is provided in the form of the above three data format for whole of South Korea and each administrative district. All of these topographic/hydrological analysis data can be downloaded for free from the environment big data platform (www.bigdata-environment.kr).

Citations

Citations to this article as recorded by

Predictors of Unilateral Arm Lymphedema in Non-obese Locoregionally Advanced Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy, Modified Radical Mastectomy, and Postoperative Irradiation
Surjeet Dwivedi, Amiy Arnav, Varun Kumar Agarwal, S K Deshpande, Rohit Sharma, Naresh Saidha
European Journal of Breast Health.2024; 20(2): 149. CrossRef
Need for Staging Investigations in Newly Diagnosed Breast Cancer: Establishing Local Guidelines for Radiological Staging in Bahrain
Ahmed Adel Alkazaz, Noora Fuad Ali, Ahmed Zuhair Salman, Sayed Ali Almahari, Tareq Hamed Altaei, Wegdan Zaki Albati, Hisham Mustafa Habib, Aysha Adnan Alsadoon, Noor Ali Almawlani, Fatema Ali Alkhabbaz, Raja Eid, Hussain Adnan Abdulla
European Journal of Breast Health.2024; 20(2): 136. CrossRef
Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii
Suci Ramayanti, BongChan Kim, Sungjae Park, Chang-Wook Lee
GIScience & Remote Sensing.2024;[Epub] CrossRef
Construction of Topographic/Hydrologic Data using DEM and its Service 2
Saro Lee, Minju Kim
GEO DATA.2021; 3(4): 1. CrossRef

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