Disaster management is a global challenge, but disasters do not affect men and women equally. In most of the world’s disasters, more females are impacted than males, and in Afghanistan the disparity between female and male victims is even greater. This study identifies and maps the relationships between the factors that make Afghan rural women more vulnerable to natural hazard-induced disasters. Data for this study were obtained through focus group discussions with rural women and men, as well as person-to-person interviews with employees of government and nongovernmental organizations at the national and local levels in Afghanistan. The study uses Grounded Theory and Interpretive Structural Modeling, not widely used before for this type of study, to analyze the data collected and to map the factors of vulnerability identified and their relationships. In agreement with previous studies, our findings show that insufficient disaster education, inadequate protection measures, and powerful cultural issues, both pre- and post-disaster, increase women’s vulnerability during and after disasters. In particular, cultural issues play a role after disasters by affecting women’s security, access to disaster aid, and health care. The study also found that perception regarding these cultural issues and how they affect women during disasters differs among men and women. Finally, by using Interpretive Structural Modeling, we show how the importance of the factors and their interrelationships change in pre-disaster and post-disaster situations. We conclude the article with some policy recommendations such as finding ways to allow women to participate in disaster planning activities and decision-making processes related to disaster risk reduction, as well as securing dedicated funds for the mainstreaming of gender in disaster risk reduction policies in Afghanistan.

Natural hazard-triggered technological accidents (Natechs) refer to accidents involving releases of hazardous materials (hazmat) triggered by natural hazards. Huge economic losses, as well as human health and environmental problems are caused by Natechs. In this regard, learning from previous Natechs is critical for risk management. However, due to data scarcity and high uncertainty concerning such hazards, it becomes a serious challenge for risk managers to detect Natechs from large databases, such as the National Response Center (NRC) database. As the largest database of hazmat release incidents, the NRC database receives hazmat release reports from citizens in the United States. However, callers often have incomplete details about the incidents they are reporting. This results in many records having incomplete information. Consequently, it is quite difficult to identify and extract Natechs accurately and efficiently. In this study, we introduce machine learning theory into the Natech retrieving research, and a Semi-Intelligent Natech Identification Framework (SINIF) is proposed in order to solve the problem. We tested the suitability of two supervised machine learning algorithms, namely the Long Short-Term Memory (LSTM) and the Convolutional Neural Network (CNN), and selected the former for the development of the SINIF. According to the results, the SINIF is efficient (a total number of 826,078 records were analyzed) and accurate (the accuracy is over 0.90), while 32,841 Natech reports between 1990 and 2017 were extracted from the NRC database. Furthermore, the majority of those Natech reports (97.85%) were related to meteorological phenomena, with hurricanes (24.41%), heavy rains (19.27%), and storms (18.29%) as the main causes of these reported Natechs. Overall, this study suggests that risk managers can benefit immensely from SINIF in analyzing Natech data from large databases efficiently.

Effective risk communication is essential for disaster risk management. Apart from empowering communities to make informed risk choices, risk information disclosure can also drastically enhance their disaster preparedness, especially concerning conjoint scenarios of technological and natural hazards (Natech). A fundamental precondition is the actual demand for such information. This study ventures to assess whether residents around Osaka Bay have this demand, or “appetite,” for risk information disclosure, as well as to understand their communicative behavior and perceived challenges in the Japanese context through the prism of the Situational Theory of Problem Solving. To test this framework under realistic conditions, data were collected through a household questionnaire survey from two urban areas near industrial complexes in Osaka Bay. The results show that identifying Natech risk information deficiency as a problem was not a statistically significant predictor for individuals’ motivation to communicate. However, their motivation increased as their perceived personal involvement with the situation rose, while the perceived obstacles in doing something about it exerted a negative influence on their motivation. Individuals’ motivation intensified their communicative actions to solve this problem. Public segmentation underscored the elevated public perceptions concerning the issue of risk information deficiency in nearly nine out of ten respondents. These findings indicate a strong community appetite for chemical and Natech risk information, which subsequently led to high situational motivation to engage in communicative action, particularly information acquisition. Risk management policy is suggested to focus on introducing chemical risk information disclosure regulatory initiatives to encourage citizen engagement.