A case study for the integration of predictive mineral potential maps

This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.     

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.     

Effect of shipping activity on warming trends in the Canadian Arctic

This paper presents a detailed account of the effect of shipping activity on the increasing trends of air temperatures in the Canadian Arctic region for the period of 1980–2018. Increasing trend of temperature has gained significant attention with respect to shipping activities and sea ice area in the Canadian Arctic. Temperature, sea ice area and shipping traffic datasets were investigated, and simple linear regression analyses were conducted to predict the rate of change(per decade) of the average temperature, considering winter(January) and summer(July) seasons. The results indicate that temperature generally increased over the studied region. Significant warming trend was observed during July, with an increase of up to 1℃, for the Canadian Arctic region. Such increasing trend of temperature was observed during July from the lower to higher latitudes. The increase in temperature during July is speculated to increase the melting of ice. Results also show a decline in sea ice area has a significant positive effect on the shipping traffic, and the numbers of marine vessel continue to increase in the region. The increase in temperature causes the breaking of sea ice due to shipping activities over northern Arctic Canada.     

Ensemble of ground subsidence hazard maps using fuzzy logic

Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.     

Spatial and temporal change in landslide hazard by future climate change scenarios using probabilistic-based frequency ratio model

The aims of this study were to apply, verify and compare a frequency ratio model for landslide hazards, considering future climate change and using a geographic information system in Inje, Korea. Data for the future climate change scenario (A1B), topography, soil, forest, land cover and geology were collected, processed and compiled in a spatial database. The probability of landslides in the study area in target years in the future was then calculated assuming that landslides are triggered by a daily rainfall threshold. Landslide hazard maps were developed for the two study areas, and the frequency ratio for one area was applied to the other area as a cross-check of methodological validity. Verification results for the target years in the future were 82.32–84.69%. The study results, showing landslide hazards in future years, can be used to help develop landslide management plans.     

Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model

An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor??s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09?%. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration.