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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.

Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application with high-resolution remote sensing data obtained by the Kompsat-3 satellite. Land and forest cover maps were used as base data to construct accurate deep-learning datasets of deforested areas at high spatial resolution, and digital maps and a softwood database were used as reference data. Sites were classified into forest and non-forest areas, and a total of 13 areas (2 forest and 11 non-forest) were selected for analysis. Overall, U-Net was more accurate than SegNet (74.8% vs. 63.3%). The U-Net algorithm was about 11.5% more accurate than the SegNet algorithm, although SegNet performed better for the hardwood and bare land classes. The SegNet algorithm misclassified many forest areas, but no non-forest area. There was reduced accuracy of the U-Net algorithm due to misclassification among sub-items, but U-Net performed very well at the forest/non-forest area classification level, with 98.4% accuracy for forest areas and 88.5% for non-forest areas. Thus, deep-learning modeling has great potential for estimating human-induced deforestation in mountain areas. The findings of this study will contribute to more efficient monitoring of damaged mountain forests and the determination of policy priorities for mountain area restoration.

Water-related disasters occur frequently worldwide and are strongly affected by a climate. Synthetic aperture radar (SAR) satellite images can be effectively used to monitor and detect damage because these images are minimally affected by weather. This study analyzed changes in water quantity and flooded area caused by the collapse of the Xe-Pian Xe-Namnoy Dam in Laos on 23 July 2018, using Sentinel-1 ground range detected (GRD) images. The collapse of this dam gained worldwide attention and led to a large number of casualties at least 98 people, as well as enormous economic losses. Thus, it is worth noting that this study quantitatively analyzed changes in both the Hinlat area, which was flooded, and the Xe-Namnoy reservoir. This study aims to suggest a practical method of change detection which is to simply compute flood extent and water volume in rapidly analysis. At first, a α -stable distribution was fitted to intensity histogram for removing the non-water-affected pixels. This fitting differs from other typical histogram fitting methods, which is applicable to histograms with two peaks, as it can be applied to histograms with not only two peaks but also one peak. Next, another type of threshold based on digital elevation model (DEM) data was used to correct for residual noise, such as speckle noise. The results revealed that about 2.2 × 108 m3 water overflowed from the Xe-Namnoy reservoir, and a flooded area of about 28.1 km3 was detected in the Hinlat area shortly after the dam collapse. Furthermore, the water quantity and flooded area decreased in both study areas over time. Because only SAR GRD images were used in this study for rapid change detection, it is possible that more accurate results could be obtained using other available data, such as optical images with high spatial resolution like KOMPSAT-3, and in-situ data collected at the same time.

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.

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.

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.

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.

Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables.

Heat island cooling strategies (HICSs) are used to mitigate urban heat island phenomena and adapt to climate change as proposed by the U.S. Environmental Protection Agency (EPA), the Intergovernmental Panel on Climate Change (IPCC), and the World Health Organization (WHO). This study investigated urban heat island reduction and assessed the cooling effect of HICSs under various temporal and spatial conditions in urban areas. The study area was the Mugye-dong urban area in South Korea. To identify the effectiveness of heat island cooling strategies (HICSs), unmanned aerial vehicle (UAV)-based remote sensing and microclimate sensors were used to generate land cover, sky view factor (SVF) distribution, and land surface temperature (LST) maps of the study area. Differences in cooling effect according to spatial density (SD) were identified by dividing the SVF into five intervals of 0.2. Temporal changes were investigated throughout the day and under cloudiness-based meteorological conditions affected by solar radiation or less affected by solar radiation. Lower SD was associated with a greater cooling effect; meteorological conditions affected by solar radiation had a stronger cooling effect. The variation of the daytime cooling effect increased with decreasing SD. The difference in cooling effect between morning and afternoon was <1 °C under conditions less affected by solar radiation. Under conditions affected by solar radiation, the maximum temperatures were −6.716 °C in urban green spaces and −4.292 °C in shadow zones, whereas the maximum temperature was −6.814 °C in ground-based albedo modification zones; thus, differences were greater under conditions affected by solar radiation than under conditions less affected by solar radiation. As a result, it was found that HICS show a high cooling effect, high diurnal variation, and high morning-afternoon deviation under weather conditions with low SD and under conditions affected by solar radiation. This study quantitatively calculated the cooling effect of HICSs applied in urban areas under various spatiotemporal conditions and compared differences by technology. Accordingly, it is believed that it will serve as a basis for supporting the practical effects of the concepts presented by international organizations for climate change adaptation.

Remote sensing and AI models have been utilized for monitoring Chlorophyll-a (Chl-a), a primary indicator of eutrophication across broad water bodies. Previous studies have primarily relied on optical remote sensing data for assessing Chl-a’s spectral characteristics. Synthetic-aperture radar (SAR) data, which contain valuable information about surface algae containing Chl-a, remains underutilized despite its high potential for improving Chl-a retrieval accuracy. Therefore, this study aims to develop a Convolutional neural network (CNN) based Chl-a retrieval model utilizing both SAR data and optical data in Korean lakes. The model dataset was established by acquiring Chl-a concentration data and Sentinel-1/2 imagery from the Copernicus Open Access Hub. The CNN model trained on both optical and SAR data exhibited superior performance (R2 = 0.7992, RMSE = 10.3282 mg/m3, RPD = 2.2315) compared with the model trained exclusively on optical data. Moreover, SAR data exhibited moderate variable importance among all variables, demonstrating their efficacy as input variables for Chl-a concentration estimation. Furthermore, the CNN model estimated Chl-a concentrations with a spatial distribution that matched the observed spatial heterogeneity of Chl-a concentrations. These results are expected to serve as a foundation for future research on remote monitoring of Chl-a using such data.