Moung-Jin Lee

Ensemble-Based Landslide Susceptibility Maps in Jinbu Area, Korea

Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS

Recently there has been an increasing occurrence of flooded area in Korea. Most of these flooded area occurred roadside in the city or residential areas. For predictive flooded area susceptibility mapping, this study applied and verified probability model, the frequency ratio at Busan, Korea, using a Geographic Information System (GIS) and Statistical methods. Flooded areas were identified in the study area of field surveys, and maps of the topography, geology, landcover and green infrastructure were constructed to spatial database. Using this analysis results, part of urban planning can find ideal locations for GIS which are needed. This result expects that this planning framework can bring flood mitigation of city.

Landslide hazard mapping considering rainfall probability in Inje, Korea

This study evaluated the landslide hazard at Inje, Korea, using a geographic information system (GIS) and rainfall probabilistic data. The locations of landslides were identified in the study area by aerial photograph interpretation and field surveys. Data about rainfall probability, topography, and geology were collected, processed, and compiled in a spatial database using GIS. Then, the probability of landslides in the study area in recurrence interval years in the future was calculated assuming that landslides are triggered by a daily rainfall of 202 mm or a three-day cumulative rainfall of 449 mm. Twelve factors that influence landslide occurrence were chosen from a database of topography, soil, and forest cover. Landslide susceptibilities were analysed and mapped according to these landslide-occurrence factors, employing the frequency ratio method. Of the total landslide locations, 50% were used for hazard analysis and the remaining 50% were used for model validation. Validation results for the daily rainfall of 202 mm and three-day cumulative rainfall of 449 mm for recurrence interval years were from 89.22% to 91.80% and from 89.38% to 93.80%, respectively. This analysis of landslide hazards took rainfall probability into account. Rainfall, including heavy rainfall, is expected to increase in the future.

Landslide susceptibility mapping by using an adaptive neuro-fuzzy inference system (ANFIS)

This paper applied an adaptive neuro-fuzzy inference system (ANFIS) based on a geographic information system (GIS) environment using landslide-related factors and location for landslide susceptibility mapping. Landslide-related factors such as slope, soil texture, wood type, lithology and density of lineament were extracted from topographic, soil, forest and lineament maps. Landslide locations were identified from interpretation of aerial photographs and field surveys. Landslide-susceptible areas were analyzed by the ANFIS method and mapped using occurrence factors. In particular, we applied various membership functions (MFs), and analysis results were verified by using the landslide location data. The predictive maps using triangular, trapezoidal, and polynomial MFs were the best individual MFs for modeling landslide susceptibility maps (84.96% accuracy), proving that ANFIS could be very effective in modeling landslide susceptibility mapping.

Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea

We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results.

Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees

As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides.

Research Trend Analysis of Geospatial Information in South Korea Using Text-Mining Technology

The purpose of this study was to analyze geospatial information (GI) research trends using text-mining techniques. Data were collected from 869 papers found in the Korea Citation Index (KCI) database (DB). Keywords extracted from these papers were classified into 13 GI domains and 13 research domains. We conducted basic statistical analyses (e.g., frequency and time series analyses) and network analyses, using such measures as frequency, degree, closeness centrality, and betweenness centrality, on the resulting domains. We subdivided the most frequent GI domain for more detailed analysis. Such processes permit an analysis of the relationships between the Research Fields associated with each GI. Our time series analysis found that the Climate and Satellite Image domain frequencies continuously increased. Satellite Image, General-Purpose Map, and Natural Disaster Map in the GI domain and Climate and Natural Disaster in the Research Field domain appeared in the center of the GI-Research Field network. We subdivided the Satellite Image domain for detailed analysis. As a result, LANDSAT, KOMPSAT, and MODIS displayed high scores on the frequency, degree, closeness centrality, and betweenness centrality indices. These results will be useful in GI-based interdisciplinary research and the selection of new research themes.