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نتیجه جستجو - natural disasters

تعداد مقالات یافته شده: 23
ردیف عنوان نوع
1 Knowledge management and natural disaster preparedness: A systematic literature review and a case study of East Lombok, Indonesia
مدیریت دانش و آمادگی برای بلایای طبیعی: مروری بر ادبیات سیستماتیک و مطالعه موردی شرق لومبوک، اندونزی-2021
Disaster impacts can be significantly reduced with disaster preparedness. Knowledge management is one of the building blocks of disaster preparedness. This paper comprehends the current state of the literature on knowl- edge management in community preparedness towards natural disasters. The paper identifies and develops a categorization of community-related knowledge management in disaster preparedness using a systematic liter- ature review. Subsequently, the categorization is utilized in a case study to determine if community-related knowledge management in the preparedness phase can improve communities’ responses in the event of natu- ral disasters. The case study was conducted in the Lombok Island community of Indonesia, which experienced two major earthquakes in mid-2018 and early 2019. The results show that knowledge transfer and creation towards and among the Lombok community increased after the mid-2018 earthquake. Consequently, the com- munity was better able to respond to the early-2019 earthquake. Better disaster preparedness activity designs are crucial in attracting participation and motivating residents to be more prepared.
keywords: عملیات بشردوستانه | مرحله آمادگی | مدیریت دانش | فاجعه با شروع ناگهانی | زمین لرزه | Humanitarian operations | Preparedness phase | Knowledge management | Sudden-onset disaster | Earthquake
مقاله انگلیسی
2 A Review on Early Wildfire Detection from Unmanned Aerial Vehicles using Deep Learning-Based Computer Vision Algorithms
035-S0165168421003467-2021
Wildfire is one of the most critical natural disasters that threaten wildlands and forest resources. Traditional firefighting systems, which are based on ground crew inspection, have several limits and can expose firefightersâĂŹ lives to danger. Thus, remote sensing technologies have become one of the most demanded strategies to fight against wildfires, especially UAV-based remote sensing technologies. They have been adopted to detect forest fires at their early stages, before becoming uncontrollable. Autonomous wildfire early detection from UAV-based visual data using different deep learning algorithms has attracted significant interest in the last few years. To this end, in this paper, we focused on wildfires detection at their early stages in forest and wildland areas, using deep learning-based computer vision algorithms to prevent and then reduce disastrous losses in terms of human lives and forest resources.
Keywords: Computer Vision | Deep Learning | Aerial Images Processing | Wildfire Detection system | Smoke Detection system | Unmanned Aerial Vehicle
مقاله انگلیسی
3 Holistic cognitive conflict chain management framework in supply chain management
چارچوب جامع مدیریت زنجیره تعارض شناختی در مدیریت زنجیره تامین-2021
Closed-loop supply chains (CLSCs) have received considerable attention because of various economic and regulatory factors. A CLSC is characterized by more complicated network structures and higher uncertainties compared to traditional supply chain networks. Therefore, reliable CLSCs are being increasingly emphasized in academic circles due to the vast impacts of disruptions such as natural disasters and terrorist attacks. This paper studies a reliable location-inventory problem in a CLSC considering the mutual effects between failures of forward and reverse distribution centers (DCs) when they are co-located. The disruption probability of a co-located forward DC is different from that of a standalone forward DC, i.e., probabilistic disruptions are dependent on facility type. The problem is formulated as a nonconvex mixed-integer programming problem. A decomposition approach based on the outer approximation (DOA) algorithm is proposed to address the resulting model. The algorithm alternately solves relaxed master problems (mixed-integer linear programs, MILPs) and two nonlinear programming (NLPs) problems. Extensive numerical experiments are conducted to evaluate the performance of the proposed solution approach, after which managerial insights are explored.
Keywords: Closed-loop supply chain | Reliable location-inventory problem | Nonconvex optimization | Outer approximation
مقاله انگلیسی
4 A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains
یک مدل بهینه سازی سطح دو فازی برای توزیع امداد پس از فاجعه چند دوره ای در زنجیره های تأمین بشردوستانه پایدار-2021
In the aftermath of large-scale natural disasters, supply shortage and inequitable distribution cause various losses, hindering humanitarian supply chains’ performance. The optimal decisions are difficult due to the complexity arising from the multi-period post-disaster consideration, uncertainty of supplies, hierarchal decision levels and conflicting objectives in sustainable humanitarian supply chains (SHSCs). This paper formulates the problem as a fuzzy tri-objective bi-level integer programming model to minimize the unmet demand rate, potential environmental risks, emergency costs on the upper level of decision hierarchy and maximize survivors’ perceived satisfaction on the lower level of decision hierarchy. A hybrid global criterion method is devised to incorporate a primal-dual algorithm, expected value and branch-and-bound approach in solving the model. A case study using data from the Wenchuan earthquake is presented to evaluate the proposed model. Study results indicate that the hybrid global criterion method guides an optimal strategy for such a complex problem within a reasonable computational time. More attention should be attached to the environmental and economic sustainability aspects in SHSCs after golden rescue stage. The proposed bi-level optimization model has the ad- vantages of reducing the total unmet demand rate, total potential environmental risks and total emergency costs. If the decision-agents with higher authorities act as the leaders with dominant power in SHSCs, the optimal decisions, respectively taking hierarchical and horizontal relationships into account would result in equal performance.
Keywords: Multi-period post-disaster relief distribution | Hierarchical decisions | Sustainable humanitarian supply chains | Fuzzy bi-level integer programming model | Hybrid global criterion method
مقاله انگلیسی
5 An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network
ارزیابی فاجعه احتمالی در زنجیره تأمین نفت و گاز با استفاده از شبکه اعتقادی بیزی-2021
The oil and gas supply chain (OGSC) is considered to have one of the most significant stakes in the U.S. economy because of its interconnectedness with supply chains in other sectors, such as health and medicine, food, heavy manufacturing, and services. While oil and gas development is expanding exponentially, various factors ranging from man-made to natural disasters can hinder OGSC processes, which, in turn, can result in inefficient and costly operations in other sectors. This study presents a Bayesian Network (BN) model to predict and assess disasters in the OGSC based on seven main factors: technical, economic, social, political, safety, environmental, and legal. BBN is a probabilistic graphical model that is predominantly used in risk analysis to illustrate and assess probabilistic relationships among different variables. To draw meaningful managerial insights into the proposed model, sensitivity analysis and belief propagation are used. The results indicate that of the seven factors responsible for OGSC disasters, technical factors have the highest impact while legal and political factors have the lowest.
Keywords: Oil and gas | Supply chain | Disaster assessment | Bayesian network | Resilience
مقاله انگلیسی
6 An efficient interactive framework for improving resilience of power-water distribution systems with multiple privately-owned microgrids
یک چارچوب تعاملی کارآمد برای بهبود مقاومت در برابر سیستم های توزیع آب و انرژی با چندین میکروگرید متعلق به بخش خصوصی-2020
Resilience improvement of power distribution networks against natural disasters is an important problem. Water network similar to other important infrastructures depends on power networks. In this paper, resilience improvement is defined as increasing the users’ accessibility to water and power after natural disasters. Microgrids with appropriate operation can provide energy to restore disconnected loads in distribution networks. In the proposed interactive framework, a stochastic energy management program for microgrids is designed that not only determines the amount of energy can be delivered to distribution systems, but also considers the reliability of local loads during emergency conditions. Each microgrid provides a list of bid-quantity energy blocks to the distribution system operator (DSO) during the emergency period. Then, the DSO chooses the best plan to restore disconnected loads considering inaccessibility values to power and water and also the damage of power and water distribution networks. Demand response actions in microgrids are also considered as effective tools for the energy management program, and their impact on the distribution system resilience is investigated. The proposed model is tested on the modified IEEE 33-bus distribution system with multiple microgrids, and the effectiveness of the proposed method is validated accordingly.
Keywords: Microgrids | Natural disasters | Resilience | Stochastic linear programming | Water network
مقاله انگلیسی
7 Detection of flood disaster system based on IoT, big data and convolutional deep neural network
تشخیص سیستم بحرانی سیل بر اساس اینترنت اشیا، داده های بزرگ و شبکه عصبی عمیق پیچشی-2020
Natural disasters could be defined as a blend of natural risks and vulnerabilities. Each year, natural as well as human-instigated disasters, bring about infrastructural damages, distresses, revenue losses, injuries in addition to huge death roll. Researchers around the globe are trying to find a unique solution to gather, store and analyse Big Data (BD) in order to predict results related to flood based prediction system. This paper has proposed the ideas and methods for the detection of flood disaster based on IoT, BD, and convolutional deep neural network (CDNN) to overcome such difficulties. First, the input data is taken from the flood BD. Next, the repeated data are reduced by using HDFS map-reduce (). After removal of repeated data, the data are pre-processed using missing value imputation and normalization function. Then, centred on the pre-processed data, the rule is generated by using a combination of attributes method. At the last stage, the generated rules are provided as the input to the CDNN classifier which classifies them as a) chances for the occurrence of flood and b) no chances for the occurrence of a flood. The outcomes obtained from the proposed CDNN method is compared parameters like Sensitivity, Specificity, Accuracy, Precision, Recall and F-score. Moreover, when the outcomes is compared other existing algorithms like Artificial Neural Network (ANN) & Deep Learning Neural Network (DNN), the proposed system gives is very accurate result than other methods.
Keywords: Hadoop distributed file system (HDFS) | Convolutional deep neural network (CDNN) | Normalization | Rule generation | Missing value imputation
مقاله انگلیسی
8 Earthquakes, fear of failure, and wellbeing: An insight from Minangkabau entrepreneurship
زلزله ، ترس از شکست و رفاه: بینشی از کارآفرینی مینانگ کابائو-2020
Earthquakes have become a constant threat in West Sumatra, Indonesia, with the most recent occurring in 2009.This phenomenon has been observed to be due to the inhabitation of people, predominantly the Minangkabau ethnic group in the “ring of fire,” which potentially causes the megathrust earthquakes and arguably shaped entrepreneurial behaviors. Therefore, the objective of this study was to examine the relationship between earthquake impact, preparedness for megathrust, fear of failure, Small Medium Enterprise (SME) financial performance, and entrepreneurs’ wellbeing. Furthermore, the fear of failure was regarded as a construct which significantly shaped the responses of entrepreneurs towards natural disasters. This investigation adopted a quantitative approach, using Smart PLS, to survey 120 small and medium enterprises affected by the 2009 West Sumatra’s earthquake. The results showed the post-earthquake impact was positively and significantly related to fear of failure while the relationships between fear of failure, financial performance, and well-being of SME were also established. Moreover, the context of Minangkabau as a completely Muslim society generated arguments regarding religiosity and organizational resilience. These factors were discovered to have influenced entrepreneurship towards making a significant contribution to the body of knowledge in disaster entrepreneurship studies.
Keywords: Earthquakes | Fear of failure | Entrepreneurs’ wellbeing | Preparedness of megathrust earthquakes | Disaster entrepreneurship | Organizational resilience
مقاله انگلیسی
9 Banking stability, natural disasters, and state fragility: Panel VAR evidence from developing countries
ثبات بانکی ، بلایای طبیعی و شکنندگی ایالت: شواهد پانل VAR از کشورهای در حال توسعه-2019
Panel VAR methodology is used in this study to empirically evaluate the effects of natural disasters and state fragility on economic and financial dimensions in developing countries such as GDP per capita, banking and financial system deposits, banks’ Z-scores, and non-performing loans. Results based on three panels of up to 66 countries and 17 years of annual data indicate that natural disasters and state fragility may cause significant economic and financial disruption in low-income and middle-income countries. Shocks from natural disasters seem to be temporary and detrimental only to non-performing loans, while shocks from state fragility appear to be permanent and to create detrimental economic and financial feedback loops.
Keywords: Banking stability | GDP per capita | Natural disasters | State fragility
مقاله انگلیسی
10 The impact of natural disasters on the banking sector: Evidence from hurricane strikes in the Caribbean
تأثیر بلایای طبیعی بر بخش بانکی: شواهدی از اعتصابات طوفان در کارائیب-2019
While natural disasters cause considerable damage and a number of studies have attempted to investigatethe nature and quantify the magnitude of these losses, there is a paucity of empirical evidence on theimpact on the banking sector. In this paper we construct a panel of quarterly banking data and historicallosses due to hurricane strikes for islands in the Eastern Caribbean to econometrically investigate theimpact of these natural disasters on the banking industry. Our results suggest that, following a hurricanestrike, banks face deposit withdrawals and experience a negative funding shock to which they respondby reducing the supply of lending and by drawing on liquid assets. There are no signs of deterioration inloan defaults and bank capital. Therefore, the withdrawal and use of deposits rather than an expansionin credit appears to play a significant role in funding post hurricane recovery in the region. This points tothe importance of an active reserve requirement policy
Keywords:Banking sector | Natural disasters | Small island economiesa
مقاله انگلیسی
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