An improved seismic vulnerability index methodology is formulated on the basis of eight modeled parameters that are designed according to earthquake-resistant design concept and derived from the empirical vulnerability index that was initiated by the Group of National Defense against earthquake, named as GNDT Level approach. The results indicated that there is a good correlation between the analytical modeling approach and the observed fragility features during in-situ field investigations. To prove that the proposed methodology is accurate and reliable, the verification of the current methodology is performed through the experimental testing related to school building damages during Ranau Earthquake.

Rapid visual screening is a quick and simple approach often used by researchers to estimate the seismic vulnerability of buildings in an area. In this study, preliminary seismic vulnerability assessment of 500 buildings situated at Northern and Eastern George Town, Malaysia, was carried out by utilizing a modified FEMA-154 (2002) method that suits Malaysian conditions. Data were collected from online sources via Google Maps and Google Earth instead of traditional surveying data collection through street screening. The seismic assessment analysis of this study was based on the RVS performance score and the damage state classification for each building typology. This approach generates, for each building, a final performance score based on governing parameters such as structural resisting system, height, structural irregularities, building age, and soil type. The findings revealed the immediate need for effective seismic mitigation strategies, as 90% of the studied buildings required a further detailed analyses to pinpoint their exact seismic vulnerability performance. Most of the surveyed buildings were predicted to experience moderate-to-substantial damage, with 220 out of 500 being classed as damage state 2 (D2) and damage state 3 (D3). A GIS map, “RVS Malaysian Form-George Town Area”, was generated via ArcGIS and shared with the public to provide vital information for further research.

Forest fires caused by different environmental and human factors are responsible for the extensive destruction of natural and economic resources. Modern machine learning techniques have become popular in developing very accurate and precise susceptibility maps of various natural disasters to help reduce the occurrence of such calamities. The present study has applied and tested multiple algorithms to map the areas susceptible to wildfire in the Mediterranean Region of Turkey. Besides, the performance of XGBoost, CatBoost, Gradient Boost, AdaBoost, and LightGBM methods for wildfire susceptibility mapping is also examined. The results have revealed the higher testing accuracy of CatBoost (95.47%) algorithm, followed by LightGBM (94.70%), XGBoost (88.8%), AdaBoost (86.0%), and GBM (84.48%) algorithms. Resultant wildfire susceptibility maps provide proper inventories for forest engineers, planners, and local governments for future policies regarding disaster management in Turkey.