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, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential.

In recent years, machine learning techniques have been increasingly applied to assessment of various natural disasters, including landslides and floods. Machine learning techniques can be used to make predictions based on the relationships among events and their influencing factors. In this study, a machine learning approach were applied based on landslide location data in a geographic information system environment. Topographic maps were used to determine the topographical factors. Additional soil and forest parameters were examined using information obtained from soil and forest maps. A total of 17 factors affecting landslide occurrence were selected and a spatial database was constructed. Naïve Bayes and Bayesian network models were applied to predict landslides based on selected risk factors. The two models showed accuracies of 78.3% and 79.8%, respectively. The results of this study provide a useful foundation for effective strategies to prevent and manage landslides in urban areas.

Using geographic information system (GIS) tools and data-mining models, this study analyzed the relationships between flood areas and correlated hydrological factors to map the regional flood susceptibility of the Seoul metropolitan area in South Korea. We created a spatial database of data describing factors including topography, geology, soil, and land use. We used 2010 flood data for training and 2011 data for model validation. Frequency ratio (FR) and logistic regression (LR) models were applied to 2010 flood data to determine the relationships between the flooded area and its causal factors and to derive flood-susceptibility maps, which were substantiated using the area flooded in 2011 (not used for training). As a result of the accuracy validation, FR and LR model results were shown to have 79.61% and 79.05% accuracy, respectively. In terms of sustainability, floods affect water health as well as causing economic and social damage. These maps will provide useful information to decision makers for the implementation of flood-mitigation policies in priority areas in urban sustainable development and for flood prevention and management. In addition to this study, further analysis including data on economic and social activities, proximity to nature, and data on population and building density, will make it possible to improve sustainability.

Recently, data mining analysis techniques have been developed, as large spatial datasets have accumulated in various fields. Such a data-driven analysis is necessary in areas of high uncertainty and complexity, such as estimating groundwater potential. Therefore, in this study, data mining of various spatial datasets, including those based on remote sensing data, was applied to estimate groundwater potential. For the sustainable development of groundwater resources, a plan for the systematic management of groundwater resources should be established based on a quantitative understanding of the development potential. The purpose of this study was to map and analyze the groundwater potential of Goyang-si in Gyeonggi-do province, South Korea and to evaluate the sensitivity of each factor by applying data mining models for big data analysis. A total of 876 surveyed groundwater pumping capacity data were used, 50% of which were randomly classified into training and test datasets to analyze groundwater potential. A total of 13 factors extracted from satellite-based topographical, land cover, soil, forest, geological, hydrogeological, and survey-based precipitation data were used. The frequency ratio (FR) and boosted classification tree (BCT) models were used to analyze the relationships between the groundwater pumping capacity and related factors. Groundwater potential maps were constructed and validated with the receiver operating characteristic (ROC) curve, with accuracy rates of 68.31% and 69.39% for the FR and BCT models, respectively. A sensitivity analysis for both models was performed to assess the influence of each factor. The results of this study are expected to be useful for establishing an effective groundwater management plan in the future.

The application of data mining models has become increasingly popular in recent years in assessments of a variety of natural hazards such as landslides and floods. Data mining techniques are useful for understanding the relationships between events and their influencing variables. Because landslides are influenced by a combination of factors including geomorphological and meteorological factors, data mining techniques are helpful in elucidating the mechanisms by which these complex factors affect landslide events. In this study, spatial data mining approaches based on data on landslide locations in the geographic information system environment were investigated. The topographical factors of slope, aspect, curvature, topographic wetness index, stream power index, slope length factor, standardized height, valley depth, and downslope distance gradient were determined using topographical maps. Additional soil and forest variables using information obtained from national soil and forest maps were also investigated. A total of 17 variables affecting the frequency of landslide occurrence were selected to construct a spatial database, and support vector machine (SVM) and artificial neural network (ANN) models were applied to predict landslide susceptibility from the selected factors. In the SVM model, linear, polynomial, radial base function, and sigmoid kernels were applied in sequence; the model yielded 72.41%, 72.83%, 77.17% and 72.79% accuracy, respectively. The ANN model yielded a validity accuracy of 78.41%. The results of this study are useful in guiding effective strategies for the prevention and management of landslides in urban areas.

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) methodologies have been used to identify areas prone to landslides recently. Therefore, in this study, DL methodologies, including a deep neural network (DNN), kernel-based DNN, and convolutional neural network (CNN) were used to identify areas where landslides could occur. As a detailed step for this purpose, landslide occurrence was first determined as landslide inventory through aerial photographs with comparative analysis using field survey data; a training set was built for model training through oversampling based on the landslide inventory. A total of 17 landslide influencing variables that influence the frequency of landslides by topography and geomorphology, as well as soil and forest variables, were selected to establish a landslide inventory. Then models were built using DNN, kernel-based DNN, and CNN models, and the susceptibility of landslides in the study area was determined. Model performance was evaluated through the average precision (AP) score and root mean square error (RMSE) for each of the three models. Finally, DNN, kernel-based DNN, and CNN models showed performances of 99.45%, 99.44%, and 99.41%, and RMSE values of 0.1694, 0.1806, and 0.1747, respectively. As a result, all three models showed similar performance, indicating excellent predictive ability of the models developed in this study. The information of landslides occurring in urban areas, which cause a great damage even with a small number of occurrences, can provide a basis for reference to the government and local authorities for urban landslide management.