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.

The current study examined the predictive ability of discrimination-related variables, coping mechanisms, and sociodemographic factors on the psychological distress level of Korean immigrants in the U.S. amid the COVID-19 pandemic. Korean immigrants (both foreign-born and U.S.-born) in the U.S. above the age of 18 were invited to participate in an online survey through purposive sampling. In order to verify the variables predicting the level of psychological distress on the final sample from 42 states (n = 790), the Artificial Neural Network (ANN) analysis, which is able to examine complex non-linear interactions among variables, was conducted. The most critical predicting variables in the neural network were a person’s resilience, experiences of everyday discrimination, and perception that racial discrimination toward Asians has increased in the U.S. since the beginning of the COVID-19 pandemic.

Depressions due to sinkhole formation cause significant structural damages to buildings and civil infrastructure. Traditionally, visual inspection has been used to detect sinkholes, which is a subjective way and time- and labor-consuming. Remote sensing techniques have been introduced for morphometric studies of karst landscapes. This study presents a methodology for the probabilistic detection of sinkholes using LiDAR-derived digital elevation model (DEM) data. The proposed study provides benefits associated with: (1) Detection of unreported sinkholes in rural and/or inaccessible areas, (2) automatic delineation of sinkhole boundaries, and (3) quantification of the geometric characteristics of those identified sinkholes. Among sixteen morphometric parameters, nine parameters were chosen for logistic regression, which was then employed to compute the probability of sinkhole detection; a cutoff value was back-calculated such that the sinkhole susceptibility map well predicted the reported sinkhole boundaries. According to the results of the LR model, the optimal cutoff value was calculated to be 0.13, and the area under the curve (AUC) of the receiver operating characteristic curve (ROC) was 0.90, indicating the model is reliable for the study area. For those identified sinkholes, the geometric characteristics (e.g., depth, length, area, and volume) were computed.

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.

Ground subsidence is one of geohazards, particularly in urban environment due to its negative impact to nearby buildings and civil infrastructures. This study investigates the amount and spatial distribution of ground subsidence in Incheon City, South Korea, where ground excavation was made. Interferometric Synthetic Aperture Radar (InSAR) was used to monitor surface deformation around the excavation site. The study analyzed subsidence during an excavation project using Sentinel-1b satellite data, and the InSAR results were compared with traditional leveling methods and field measurements. Notable subsidence during the early construction phase was identified, which demonstrates the effectiveness of InSAR in urban environment. The research contributes to understanding of the excavation-inducing ground subsidence due to the reclamation layer.

The incorporation of waste ground glass powder (GGP) in concrete as a partial replacement of cement offers significant environmental benefits, such as reduction in CO₂ emission from cement manufacturing and decrease in the use of colossal landfill space. However, concrete is a heterogeneous material, and the prediction of its accurate compressive strength is challenging due to the inclusion of several non-linear parameters. This study explores the utilization of different machine learning (ML) algorithms: linear regression (LR), ElasticNet regression (ENR), a K-Nearest Neighbor regressor (KNN), a decision tree regressor (DT), a random forest regressor (RF), and a support vector regressor (SVR). A total of 187 sets of pertinent mix design experimental data were collected to train and test the ML algorithms. Concrete mix components such as cement content, coarse and fine aggregates, the water–cement ratio (W/C), various GGP chemical properties, and the curing time were set as input data (X), while the compressive strength was set as the output data (Y). Hyperparameter tuning was carried out to optimize the ML models, and the results were compared with the help of the coefficient of determination (R²) and root mean square error (RMSE). Among the algorithms considered, SVR demonstrates the highest accuracy and predictive capability with an R² value of 0.95 and RMSE of 3.40 MPa. Additionally, all the models exhibit R² values greater than 0.8, suggesting that ML models provide highly accurate and cost-effective means for evaluating and optimizing the compressive strength of GGP-incorporated sustainable concrete.