Natural Hazards Research最新文献

West Bengal is situated primarily in the Surma Valley at the foothills of the Himalayas and near the western foreland of the Assam-Arakan Orogenic Belt. Several low to moderate-magnitude earthquakes are felt in the region frequently. In this study, we use integrated multi-criteria decision-making (MCDM) models to assess the seismic vulnerability in West Bengal. Twenty-four parameters that were susceptible to seismicity in the region have been used to evaluate geotechnical, structural, social, and physical vulnerability. The analytical hierarchy process (AHP) model was used to estimate the priorities of the parameters, which was then used to estimate seismic vulnerability using the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and the weighted sum method (WSM). The results reveal that approximately ∼17.81% of the total area and ∼65.36% population may fall under a high to very high-vulnerable zone, causing concerns for planning and disaster mitigation. The receiver operating characteristic curve estimated to validate the results, indicate that the AHP-VIKOR performs better for seismic vulnerability estimation. The results of this study may help various mitigation and planning agencies in identifying earthquake-vulnerable zones and preparing in advance for any potential large magnitude earthquakes that may occur in the region.

Improving disaster prevention, reduction, and emergency response capabilities is crucial in a country prone to frequent natural disasters. Since the release of ChatGPT, it has garnered widespread attention and sparked extensive discussions in various fields due to its powerful language processing and reasoning abilities. This paper explores the application of ChatGPT in natural disaster prevention and reduction, building upon its language capabilities. The paper examines ChatGPT's ability to gather information and its potential for disaster prevention science popularization and education. It describes the rapid response and availability of ChatGPT in natural disaster prevention and highlights its potential to assist emergency response efforts. The paper also outlines ChatGPT's assistance in the pre-disaster, during-disaster, and post-disaster phases. Additionally, it points out the current limitations and challenges in applying ChatGPT and provides prospects for future research directions in natural disaster prevention and reduction.

Due to global climate change, community resilience to natural disasters has become a high priority in environmental research. Academicians and practitioners from different disciplines and organizations include several dimensions to outline the process of building resilient communities. Although this research branch suffers from the lack of a shared theoretical and methodological consensus, many scholars publish research articles each year. Similarly, social scientists include diverse contextual humanitarian dimensions that are challenging to trace. Therefore, this study attempts to undertake a systematic review of the literature of the last 12 years (2010–2021) to outline the current trends in research methods, selected dimensions, and theoretical standpoints from the social perspective. This systematic observation of the literature identifies the recent trends in adopting research design, sampling design, and data collection techniques used for the research. The present review also traces the propensity of including major theoretical dimensions in the research. After identifying the contemporary trends in research, we find that a comprehensive multi-phase research model is necessary to initiate an effective policymaking in a specific socio-ecological context.