https://www.joem.ir/ Emergency Management en daily 1 Sat, 22 Nov 2025 00:00:00 +0330 Sat, 22 Nov 2025 00:00:00 +0330 https://www.joem.ir/article_728656.html Floods are among the most significant natural hazards, which-depending on the intensity of rainfall and other contributing factors-can cause extensive damage to urban and rural areas. These damages may include the destruction of infrastructure, harm to homes and agricultural lands, and even loss of human life. Therefore, producing flood extent maps provides valuable information for preparedness, crisis management, communication, rapid response, and risk reduction during disasters. One of the modern and effective methods for studying floods is the use of Synthetic Aperture Radar (SAR) data. These data allow for detailed analysis of flood events and accurate delineation of their extent, which can help improve future planning and support better decision-making. In this study, flood-affected areas in Sistan and Baluchestan Province during April 2024 (Farvardin 1403 in the Iranian calendar) were identified. For this purpose, the Otsu automatic thresholding method was applied to Sentinel-1 SAR images using the Google Earth Engine (GEE) web platform. The results were validated using cumulative rainfall data over a 6-day period (April 14–19, 2024), obtained from rain gauge stations. This evaluation indicated that the model performed with acceptable accuracy (R² = 0.92, RMSE = 0.06). The findings showed that the counties of Zarabad, Nikshahr, Konarak, Chabahar, Dashtiari, Sarbaz, Mehrestan, Sib, Saravan, Golshan, Khash, Mirjaveh, Iranshahr, and Zahedan were affected by the flood during this period. Additionally, salt lakes in Delgan, Bampur, and the western part of Zahedan were revived. These results can serve as a basis for improved future planning and reducing flood-related damages. They can also assist decision-makers in crisis management and disaster prevention efforts. https://www.joem.ir/article_728669.html This study aimed to identify and prioritize the variables influencing the formulation of a strategic policy-making model for electrical energy infrastructure in the Islamic Republic of Iran, utilizing the fuzzy TOPSIS multi-criteria decision-making method with a triangular fuzzy approach. The research adopted a descriptive-exploratory design with a mixed-methods (qualitative-quantitative) approach. Data were collected through library studies, analysis of upstream policy documents, interviews with energy and infrastructure experts, and semi-structured questionnaires. The findings resulted in the identification of 138 sub-components, 35 components, and 5 dimensions, categorized within a strategic policy-making model for Iran’s electrical energy infrastructure. The fuzzy TOPSIS ranking revealed that sub-components related to environmental sustainability, technology, and climatic resources were among the highest priorities. Key sub-components included "compliance with climatic resources," "facilitation of advanced technology transfer," "carbon emission reduction," and "optimal use of renewable energy resources." Additionally, components such as "international collaborations," "strategic management," "diversification of energy resource portfolios," and "ensuring energy sustainability" were ranked as high priorities. The novelty of this research lies in its comprehensive, multi-dimensional model, encompassing structural, procedural, contextual, behavioral, and directional dimensions. This model serves as an operational framework for decision-makers and energy planners in Iran to manage energy crises and enhance the resilience of critical energy infrastructure. https://www.joem.ir/article_729261.html Climate hazards pose a serious threat to global communities and ecosystems, exerting extensive impacts on the environment and natural resources. In this context, the Internet of Things (IoT) plays a significant role as an emerging technology in monitoring, analyzing, and managing these risks. This research has been conducted with the objective of investigating the role of IoT in mitigating climate hazards. The study is designed as an applied, descriptive research with a qualitative approach. To enhance validity and reliability, data triangulation methods were employed. Furthermore, to determine content validity, Lawshe's method was utilized with a sample of 12 experts and faculty members in the fields of information technology, climate, and environmental sciences. The calculated Content Validity Ratio (CVR) was 0.83, indicating very good and acceptable content validity for the data collection instrument in this research. Data collection and analysis were carried out through content analysis using the MAXQDA software, resulting in the extraction of 6 axial codes. The findings indicate that IoT, by leveraging smart sensors and network-connected systems, enables real-time data collection and plays an effective role in analyzing climate patterns, optimizing energy consumption, reducing air pollution, monitoring ecosystems, managing water resources, tracking climate changes, and providing early warnings. Early warning systems and water resource management received the highest number of open codes, highlighting the importance of IoT in these areas. Additionally, it was found that integrating IoT with emerging technologies such as artificial intelligence can enhance the accuracy of climate analyses and predictions, thereby contributing to the development of sustainable solutions. https://www.joem.ir/article_729861.html Energy security is one of the key concepts in the sustainable development of organizations, emphasizing continuous access to energy carriers at a reasonable cost. In recent years, the increasing risks associated with disruptions in energy supply have underscored the importance of comprehensive approaches to energy security management. In this study, a framework for modeling energy flow and risk assessment in the organizational energy chain is presented, utilizing global standards such as ISO 31000 and ISO 50000. Using statistical methods and uncertainty analysis, risks related to energy access were identified and quantified in both the short term (crisis scenario) and long term (general equilibrium scenarios). Subsequently, by employing event concurrency matrices and recovery maturity analysis, the "security risk" index was introduced, enabling the prioritization of executive measures to enhance organizational energy security. One of the innovations of this research is the introduction of the "security risk" index, which provides a quantitative representation of energy security by considering the probability of risk occurrence, the severity of its impact, and the organization's recovery time from crises. The results indicate that combining risk management strategies with energy resource optimization can significantly enhance organizational resilience against energy crises. Ultimately, this study offers an operational framework for organizations to systematically prioritize and implement continuous improvement measures in energy security. https://www.joem.ir/article_729866.html In the past, strategic control was regarded as a human-centered activity, heavily reliant on human judgment, experience, and intelligence. However, with the emergence of artificial intelligence-particularly in areas such as machine learning, predictive analytics, and natural language processing-we are witnessing a shift in strategic control practices towards automated and algorithm-driven systems. This paper systematically reviews articles published in the last four years concerning the role of artificial intelligence in strategic control. A search and retrieval of studies from six reputable databases identified 1,050 articles, which were screened based on exclusion criteria. Ultimately, 130 articles were selected for the review, of which 47 were relevant to the literature on the role of artificial intelligence in strategic control. The findings indicate that artificial intelligence significantly enhances decision-making processes by providing data-driven insights, predictive modeling, and scenario planning. Furthermore, AI automates routine tasks such as performance monitoring and reporting, freeing human resources for more complex strategic functions. The challenges of integrating AI into strategic control include the need for high-quality data, technical expertise, and the development of new organizational structures to accommodate AI-driven processes. Additionally, ethical concerns such as bias in AI algorithms and the lack of transparency and accountability in AI decision-making must be addressed. This paper emphasizes the importance of developing robust data management systems, reskilling employees, and implementing ethical frameworks to ensure that AI complements rather than replaces human intelligence. Consequently, AI has the potential to transform the field of strategic control by enhancing decision-making, automating key processes, and providing predictive insights. https://www.joem.ir/article_730563.html Modern large-scale, short-term, and technology-driven military conflicts inflict the most devastating impact on national security due to their intensity and sudden shock. The recent 12-day war was a clear example of such a crisis, which affected the country's defense structures. Within this context, the defense industry supply chain, as the backbone of these industries, was also damaged. Disruptions in the supply of strategic raw materials, physical damage to factories, and the assassination of specialized human capital endangered the continuous operation of this chain. Given the lack of genuine resilience in this critical chain, this research aims to develop an integrated, practical, and indigenous operational roadmap for rapid recovery, overcoming this shock, and ultimately achieving long-term resilience. To achieve this goal, a mixed-methodology was employed. First, through in-depth library studies and focus group interviews with 20 experts and managers, 19 strategies were identified and extracted into two categories: "Rapid Recovery" and "Long-Term Resilience." Then, by employing Interpretive Structural Modeling (ISM), the complex and hierarchical relationships between these strategies were analyzed, the final roadmap was drawn, and finally, the critical strategies and paths of this roadmap were determined based on sensitivity analysis. The findings revealed that the strategies are interconnected within a five-level hierarchical structure. At the most fundamental level, "Establishing a Centralized Command Post" and "Localization and Internalization" were identified as the most effective levers for transitioning from crisis to resilience. Three critical causal paths were also identified, revealing how the outputs of the rapid recovery phase transform into essential inputs for long-term resilience. The simultaneous and hierarchical focus on both rapid recovery and long-term resilience is the novel and proposed approach of this study, which can sustainably ensure the resilience of the country's defense industry supply chain against future crises.