Australia has suffered devastating wildfires recently, and is predisposed to them due to several factors, including topography, meteorology, vegetation, and ignition sources. This study utilized a geographic information system (GIS) technique to analyze and understand the factors that regulate the spatial distribution of wildfire incidents and machine learning to predict wildfire susceptibility in Sydney. Wildfire inventory data were constructed by combining the fire perimeter through field surveys and fire occurrence data gathered from the visible infrared imaging radiometer suite (VIIRS)-Suomi thermal anomalies product between 2011 and 2020 for the Sydney area. Sixteen wildfire-related factors were acquired to assess the potential of machine learning based on support vector regression (SVR) and various metaheuristic approaches (GWO and PSO) for wildfire susceptibility mapping in Sydney. In addition, the 2019–2020 “Black Summer” fire acted as a validation dataset to assess the predictive capability of the developed model. Furthermore, the information gain ratio (IGR) method showed that driving factors such as land use, forest type, and slope degree have a large impact on wildfire susceptibility in the study area, and the frequency ratio (FR) method represented how the factors influence wildfire occurrence. Model evaluation based on area under the curve (AUC) and root average square error (RMSE) were used, and the outputs showed that the hybrid-based SVR-PSO (AUC = 0.882, RMSE = 0.006) model performed better than the standalone SVR (AUC = 0.837, RMSE = 0.097) and SVR-GWO (AUC = 0.873, RMSE = 0.080) models. Thus, optimizing SVR with metaheuristics improved the accuracy of wildfire susceptibility modeling in the study area. The proposed framework can be an alternative to the modeling approach and can be adapted for any research related to the susceptibility of different disturbances.

Earthquake activities in and around the Korean Peninsula are relatively low in number and intensity compared with neighboring countries such as Japan and China. However, recent seismic activity caused great alarm and concern among citizens and government authorities, and uncovered the level of preparedness toward earthquake disasters. A survey has been conducted on 1256 participants to investigate the seismic literacy of Korean citizens, including seismic knowledge, awareness and management using a questionnaire of citizen earthquake literacy (CEL). The results declared that the citizens had low awareness and literacy, which means that they are not properly prepared for earthquake hazards. To develop an earthquake risk reduction plan and program efficiently and effectively, not only must it appropriately characterize the target audience, but also indicate high potential earthquake zones and potential earthquake damage. Therefore, this study mapped and analyzed the seismic vulnerability in southeast Korea using LogitBoost, logistic model tree (LMT), and logistic regression (LR) machine learning algorithms based on a building damage inventory map. The damaged buildings’ locations were generated after the 2017 Pohang earthquake using the damage proxy map (DPM) method from the Sentinel-1 synthetic aperture radar (SAR) data. DPMs detected coherence loss, which indicates damaged buildings in urban areas in the Pohang earthquake and shows a good correlation with the Korea Meteorological Administration (KMA) report with modified Mercalli intensity (MMI) scale values of more than VII (seven). The damage locations were randomly divided into two datasets: 50% for training the vulnerability models and 50% for validating the models in terms of accuracy and reliability. Fifteen seismic-related factors were used to construct a model of each algorithm. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of seismic vulnerability maps using the LogitBoost, LMT, and LR algorithms were 0.769, 0.851, and 0.749, respectively. We suggest that earthquake preparedness efforts should focus on reconstruction, retrofitting, renovation, and seismic education in areas with high seismic vulnerability in South Korea. The results of this study are expected to be beneficial for engineers and policymakers aiming at developing disaster risk reduction plans, policies, and programs due to future seismic activity in South Korea.

Plumas National Forest, located in the Butte and Plumas counties, has experienced devastating wildfires in recent years, resulting in substantial economic losses and threatening the safety of people. Mapping damaged areas and assessing wildfire susceptibility are necessary to prevent, mitigate, and manage wildfires. In this study, a wildfire susceptibility map was generated using a CNN and metaheuristic optimization algorithms (GWO and ICA) based on images of areas damaged by wildfires. The locations of damaged areas were identified using the damage proxy map (DPM) technique from Sentinel-1 synthetic aperture radar (SAR) data collected from 2016 to 2020. The DPMs’ depicting areas damaged by wildfires were similar to fire perimeters obtained from the California Department of Forestry and Fire Protection (CAL FIRE). Data regarding damaged areas were divided into a training set (50%) for modeling and a testing set (50%) for assessing the accuracy of the models. Sixteen conditioning factors, categorized as topographical, meteorological, environmental, and anthropological factors, were selected to construct the models. The wildfire susceptibility models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and root mean square error (RMSE) analysis. The evaluation results revealed that the hybrid-based CNN-GWO model (AUC = 0.974, RMSE = 0.334) exhibited better performance than the CNN (AUC = 0.934, RMSE = 0.780) and CNN-ICA (AUC = 0.950, RMSE = 0.350) models. Therefore, we conclude that optimizing a CNN with metaheuristics considerably increased the accuracy and reliability of wildfire susceptibility mapping in the study area.

Nur, A.-S.; Achmad, A.-R., and Lee, C.-W., 2020. Land subsidence measurement in reclaimed coastal land: Noksan using C-Band Sentinel-1 radar interferometry. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 218-223. Coconut Creek (Florida), ISSN 0749-0208.It has been 27 years since the beginning of the Noksan national industrial complex construction at downstream of the Nakdong River in Busan. The complex was built by landfilling a coastal area over one of Korea's deepest soft ground areas. Land subsidence occurred since the start of construction due to primary consolidation and secondary consolidation after the creation finished. Since secondary consolidation directly affects the structure over Noksan, it is crucial to monitor the subsidence rate. This study found that land subsidence happened in four areas of Noksan National Industrial Complex from 2017 to 2020 by applying Stamford Methods for Persistent Scatterer (StaMPS) time-series method using C-band Sentinel-1 SAR datasets from descending and ascending track. The results show land subsidence with a maximum velocity of 20.98 mm/year from descending track datasets and 20.92 mm/year from ascending track datasets. The highest rate area of land subsidence occurred in the south part of Noksan. Mean vertical deformation maps between descending and ascending track have been compared and show a good relationship with 0.93 of coefficient correlation. These approach methods in this study that using StaMPS could be used to monitor slow rate of land subsidence in reclaimed coastal land.

The southeast Texas (SETX) coastal area, owing to its unique geographical location and geological attributes, is facing a spectrum of geological challenges, such as ground subsidence, flooding, and coastal erosion. This study endeavors to evaluate the recent instances of ground subsidence and their associated rates, focusing on comprehending their implications for flooding within SETX. Employing the Persistence Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique, this study employs Sentinel-1 SAR satellite data with descending orbit observations spanning from January 2020 to March 2023. Our findings indicate that both the northwestern and eastern regions of Houston have been settled with rates up to 2 cm/year. This settlement trend is consistent with data derived from GPS and groundwater level measurements. This investigation explicitly shows the substantial temporal and spatial variations in subsidence rates, predominantly influenced by localized groundwater extraction due to urbanization (e.g., population growth, land development, etc.). By advocating for the integration of InSAR, GPS, and groundwater measurements, this research aspires to make valuable contributions toward the mitigation of subsidence and flood-related hazards in the SETX area.