Flood damage assessment (FDA) is an essential tool for evaluating flood damage, vulnerability, and risk to civil systems such as residential buildings. The outcome of an FDA depends on the spatial limits of the study and the complexity of the data. For microscale FDA, a high level of detail is required to assess flood damage. This study reviewed the existing methodologies in microscale FDA based on empirical and synthetic data selection methods for model development. The merits and challenges of these approaches are discussed. This review also proposes an integrated step for assessing the stages of FDA. This study contributes to the literature by providing insights into the methodologies adopted, particularly on a microscale basis, which has not been comprehensively discussed in the previous reviews. The findings of this study reveal that univariate modeling of flood damage is nevertheless popular among researchers. New advanced approaches, such as advanced machine learning and 3D models, are yet to gain prominence when compared with the univariate modeling that has recorded a high success. This review concludes that there is a need to adopt a combined empirical–synthetic approach in the selection of data for developing damage models. Further research is required in the areas of multivariate modeling (advanced machine learning), 3D BIM-GIS modeling, 3D visualization of damages, and projection of probabilities in flood damage predictions to buildings. These are essential for performance flood-based building designs and for promoting building resilience to flood damage.

Landslides due to rainfall are among the most destructive natural disasters and cause property damage, huge financial losses, and human deaths in different parts of the world. To plan for mitigation and resilience and to understand the relationship between the volume of soil materials debris and their associated predictors, prediction of the volume of rainfall-induced landslides is essential. The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), k-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. Models were trained and tested on a South Korean landslide dataset, with the EGB predictions yielding the highest coefficient of determination (R² = 0.8841) and the lowest mean absolute error (MAE = 146.6120 m³), followed by RF predictions (R² = 0.8435, MAE = 330.4876 m³), on the holdout set. The DNN, EGB, and RF models exhibited R² > 0.8 on both the training and the test sets. The differences in the coefficient of determination R² on the training and holdout set were 1.75 %, 7.72 %, and 12.17 % for RF, EGB, and DNN, respectively, signifying that these models could yield reliable volume estimates in adjacent areas with similar geomorphological and environmental settings. The volume of landslides was strongly influenced by slope length, maximum hourly rainfall, slope angle, aspect, and altitude. The anticipated volume of landslides can be important for land use allocation and efficient landslide risk management.

Identification and mapping of high-potential Iron Ore indicative alteration zone is important for new prospects. Here we have performed same through ASTER and Hyperion data by considering the in-situ and reported spectral signatures and Band Ratio techniques. The Minimum Noise Fraction and Pixel Purity Index have been applied for the noise reduction and pure pixel extraction from the Hyperion data. Spectral Angle Mapper algorithm has been used to identify the Hematite and Goethite exposures based on the spectral assembling. On the other hand, Ferric Iron, Ferrous Iron, Ferrous Silicates, and Ferric Oxide index have been applied on ASTER VNIR and SWIR data for demarcating alteration zones indicating subsurface occurrence of iron ore. Thereafter, the consistency index has been established between the ground truth/field observations and classified alteration through kappa statistics demonstrates the accurate identification and remote mapping of Iron Ore alteration zone in and around Joda region.

The inner shelf and coastal region of the Yellow Sea along the Korean Peninsula are frequently impacted by typhoons. The Mokpo coastal region in South Korea was significantly affected by Typhoon Soulik in 2018, the deadliest typhoon strike to the southwestern coast since Typhoon Maemi in 2003. Typhoon Soulik overran the region, causing extensive damage to the coast, shoreline, vegetation, and coastal geomorphology. Therefore, it is important to investigate its impact on the coastal ecology, landform, erosion/accretion, suspended-sediment concentration (SSC), and associated coastal changes along the Mokpo region. In this study, the net shoreline movement (NSM), normalized difference vegetation index (NDVI), fractional vegetation coverage (FVC), coastal-landform change model, normalized difference suspended-sediment index (NDSSI), and SSC–reflectance relation have been used to analyze the coastal morphodynamics over the typhoon periods. We used pre- and post-typhoon Sentinel-2 MultiSpectral Instrument (MSI) images for mapping and monitoring the typhoon effect and recovery status of the Mokpo coast through short- and medium-term coastal-change analysis. The findings highlighted the significant impacts of typhoons on coastal dynamics, wetland vegetation, and sediment resuspension along the Mokpo coast. It has been observed that typhoon-induced SSC influences shoreline and coastal morphology. The outcome of this research may provide databases to manage coastal environments and a long-term plan to restore valuable coastal habitats. In addition, the findings may be useful for post-typhoon emergency response, coastal planners, and administrators involved in the long-term development of human life.