Search results for: disaster classification

Authors: Tao Youjun, Zhao Younan

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The separation effect of fine coal can be improved by the process of pre-desliming. It was significantly enhanced when the fine coal was processed using Falcon concentrator with the removal of -45um coal slime. Ultrafine classification tests using Krebs classification cyclone with annular rinse water showed that increasing feeding pressure can effectively avoid the phenomena of heavy particles passing into overflow and light particles slipping into underflow. The increase of rinse water pressure could reduce the content of fine-grained particles while increasing the classification size. The increase in feeding concentration had a negative effect on the efficiency of classification, meanwhile increased the classification size due to the enhanced hindered settling caused by high underflow concentration. As a result of optimization experiments with response indicator of classification efficiency which based on orthogonal design using Design-Expert software indicated that the optimal classification efficiency reached 91.32% with the feeding pressure of 0.03MPa, the rinse water pressure of 0.02MPa and the feeding concentration of 12.5%. Meanwhile, the classification size was 49.99 μm which had a good agreement with the predicted value.

Keywords: hydrocyclone, ultrafine classification, slime, classification efficiency, classification size

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Authors: Sameen Masood, Muhammad Ali Jibran

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The frequent flood history in Pakistan has pronounced the need for disaster risk management. Various policies are formulated and steps are being taken by the government in order to cope with the flood effects. However, a much promising pro-active approach that is globally acknowledged is educating the masses regarding living with risk and uncertainty. Unfortunately, majority of the flood victims in Pakistan are poor and illiterate which also transpires as a significant cause of their distress. An illiterate population is not risk averse or equipped intellectually regarding how to prepare and protect against natural disasters. The current research utilizes a cross-disciplinary approach where the role of education (both formal and informal) and indigenous knowledge is explored with reference to disaster preparedness. The data was collected from the flood prone rural areas of Punjab. In the absence of disaster curriculum taught in formal schools, informal education disseminated by NGOs and relief and rehabilitation agencies was the only education given to the flood victims. However the educational attainment of flood victims highly correlated with their awareness regarding flood management and disaster preparedness. Moreover, lessons learned from past flood experience generated indigenous knowledge on the basis of which flood victims prepared themselves for any uncertainty. If the future policy regarding disaster preparation integrates indigenous knowledge and then delivers education on the basis of that, it is anticipated that the flood devastations can be much reduced. Education can play a vital role in amplifying perception of risk and taking precautionary measures for disaster. The findings of the current research will provide practical strategies where disaster preparedness through education has not yet been applied.

Keywords: education, disaster preparedness, illiterate population, risk management

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Authors: Ali Nadi, Ali Edrissi

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Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

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Authors: Kolade Olawande Owoeye, George R. S. Weir

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The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification.

Keywords: extremist, web pages, classification, semantics, posit

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Authors: A. Esra İdemen, Sinan M. Şener, Emrah Acar

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The technological paradigm of the disaster management field, especially in the case of governmental intervention strategies, is generally based on rapid and flexible accommodation solutions. From various technical solution patterns used to address the immediate housing needs of disaster victims, the adaptive re-use of existing buildings can be considered to be both low-cost and practical. However, there is a scarcity of analytical methods to screen, select and adapt buildings to help decision makers in cases of emergency. Following an extensive literature review, this paper aims to highlight key points and problem areas associated with the adaptive re-use of buildings within the disaster management context. In other disciplines such as real estate management, the adaptive re-use potential (ARP) of existing buildings is typically based on the prioritization of a set of technical and non-technical criteria which are then weighted to arrive at an economically viable investment decision. After a disaster, however, the assessment of the ARP of buildings requires consideration of different/additional layers of analysis which stem from general disaster management principles and the peculiarities of different types of disasters, as well as of their victims. In this paper, a discussion of the development of an adaptive re-use potential (ARP) assessment model is presented. It is thought that governmental and non-governmental decision makers who are required to take quick decisions to accommodate displaced masses following disasters are likely to benefit from the implementation of such a model.

Keywords: adaptive re-use of buildings, disaster management, temporary housing, assessment model

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Authors: Chaiyaset Promsri

Abstract:

Based on the synthesis of related literatures, this research found thirteen related dimensions that involved the development of natural disaster preparedness awareness including hazard knowledge, hazard attitude, training for disaster preparedness, rehearsal and practice for disaster preparedness, cultural development for preparedness, public relations and communication, storytelling, disaster awareness game, simulation, past experience to natural disaster, information sharing with family members, and commitment to the community (time of living).  The 40-item of natural disaster preparedness awareness questionnaire was developed based on these thirteen dimensions. Data were collected from 595 participants in Bangkok metropolitan and vicinity. Cronbach's alpha was used to examine the internal consistency for this instrument. Reliability coefficient was 97, which was highly acceptable.  Exploratory Factor Analysis where principal axis factor analysis was employed. The Kaiser-Meyer-Olkin index of sampling adequacy was .973, indicating that the data represented a homogeneous collection of variables suitable for factor analysis. Bartlett's test of Sphericity was significant for the sample as Chi-Square = 23168.657, df = 780, and p-value < .0001, which indicated that the set of correlations in the correlation matrix was significantly different and acceptable for utilizing EFA. Factor extraction was done to determine the number of factors by using principal component analysis and varimax.  The result revealed that four factors had Eigen value greater than 1 with more than 60% cumulative of variance. Factor #1 had Eigen value of 22.270, and factor loadings ranged from 0.626-0.760. This factor was named as "Knowledge and Attitude of Natural Disaster Preparedness".  Factor #2 had Eigen value of 2.491, and factor loadings ranged from 0.596-0.696. This factor was named as "Training and Development". Factor #3 had Eigen value of 1.821, and factor loadings ranged from 0.643-0.777. This factor was named as "Building Experiences about Disaster Preparedness".  Factor #4 had Eigen value of 1.365, and factor loadings ranged from 0.657-0.760. This was named as "Family and Community". The results of this study provided support for the reliability and construct validity of natural disaster preparedness awareness for utilizing with populations similar to sample employed.

Keywords: natural disaster, disaster preparedness, disaster awareness, Thai citizens

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Authors: Jessy Rugeyo, William Donner

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This research delves into the resilience of Rwanda's public health infrastructure amidst natural disasters, a critical issue given that the Northern Province alone has witnessed no fewer than 1500 cases of disaster ranging from floods and landslides in the last five years, with more than 200 people killed and thousands of homes destroyed, according to MINEMA. In an era where climate change escalates the frequency and intensity of such disasters, fortifying the resilience of public health systems is paramount. This study offers a comprehensive analysis of the existing state of Rwanda's public health infrastructure and its ability to manage such crises. Employing a mix of literature review, case studies, and policy analysis, the study discerns key vulnerabilities and brings to light the intricacies of disaster management in Rwanda. Case studies centered around past natural disasters in Rwanda provide critical insights into the strengths and weaknesses of the existing disaster response mechanisms. A thorough critique of related disaster management and public health infrastructure policies reveals areas of commendable practice, along with gaps calling for policy enhancements. Findings guide the proposition of targeted strategies to bolster the resilience of Rwanda's public health infrastructure. This research serves as a significant contribution to the domains of disaster studies and public health, offering valuable insights for policymakers, public health and disaster management professionals in Rwanda and similar contexts. It presents actionable recommendations for improvement, underscoring the potential for enhancing Rwanda's disaster management capacity. By advocating for the strengthening of public health infrastructure resilience, the research highlights the potential for improved public health outcomes following natural disasters, thereby showcasing significant implications for public health and disaster management in the country, particularly in the face of a changing climate.

Keywords: public health infrastructure, disaster resilience, natural disaster, disaster management, emergency preparedness, health policy

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Authors: D. Moser, D. Pinto, A. Cipriano

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A Disaster Management System (DMS) for countries with different disasters is very important. In the world different disasters like earthquakes, tsunamis, volcanic eruption, fire or other natural or man-made disasters occurs and have an effect on the population. It is also possible that two or more disasters arisen at the same time, this means to handle multi-risk situations. To handle such a situation a Decision Support System (DSS) based on multiagents is a suitable architecture. The most known DMSs deal with one (in the case of an earthquake-tsunami combination with two) disaster and often with one particular disaster. Nevertheless, a DSS helps for a better realtime response. Analyze the existing systems in the literature and expand them for multi-risk disasters to construct a well-organized system is the proposal of our work. The here shown work is an approach of a multi-risk system, which needs an architecture, and well-defined aims. In this moment our study is a kind of case study to analyze the way we have to follow to create our proposed system in the future.

Keywords: decision support system, disaster management system, multi-risk, multiagent system

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Authors: Kim Sang Hyuck, Y. Park Sung

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The outbreak of national disasters stimulates the decrease of the both internal and domestic tourism demands, causing bad effects on the hospitality industry. The effective and efficient risk management regarding national disasters are being increasingly required from the hospitality industry practitioners and the tourism policymakers. To establish the effective and efficient risk management strategy on national disasters, the most essential prerequisite condition is the correct estimation of national disasters’ effects in terms of the size and duration of the damages occurred from national disaster on hospitality industry. More specifically, the national disasters are twofold: natural disaster and social disaster. In addition, the hospitality industry has consisted of several types of business, such as hotel, restaurant, travel agency, etc. As reasons of the above, it is important to consider how each type of national disasters differently influences on the performance of each type of hospitality industry. Therefore, the purpose of this study is examining the effects of national disaster on hospitality industry in Korea based on the types of national disasters as well as the types of hospitality business. The monthly data was collected from Jan. 2000 to Dec. 2016. The indexes of industrial production for each hospitality industry in Korea were used with the proxy variable for the performance of each hospitality industry. Two national disaster variables (natural disaster and social disaster) were treated as dummy variables. In addition, the exchange rate, industrial production index, and consumer price index were used as control variables in the research model. The impulse response analysis was used to examine the size and duration of the damages occurred from each type of national disaster on each type of hospitality industries. The results of this study show that the natural disaster and the social disaster differently influenced on each type of hospitality industry. More specifically, the performance of airline industry is negatively influenced by the natural disaster at the time of 3 months later from the incidence. However, the negative impacts of social disaster on airline industry occurred not significantly over the time periods. For the hotel industry, both natural disaster and social disaster negatively influence the performance of hotel industry at the time of 5 months and 6 months later, respectively. Also, the negative impact of natural disaster on the performance of restaurant industry occurred at the time of 5 months later, as well as for both 3 months and 6 months later for the social disaster. Finally, both natural disaster and social disaster negatively influence the performance of travel agency at the time of 3 months and 4 months later, respectively. In conclusion, the types of national disasters differently influence the performance of each type of hospitality industry in Korea. These results would provide an important information to establish the effective and efficient risk management strategy for the national disasters.

Keywords: impulse response analysis, Korea, national disaster, performance of hospitality industry

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Authors: Shreya Pare, Parvin Akhter

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Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures.

Keywords: classification, hyperspectral images, large distribution margin, modified fuzzy entropy function, multilevel thresholding, tree search algorithm, hyperspectral image classification using tree search algorithm

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Authors: Sara Hasani

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This research focuses on natural sudden onset disasters characterised as ‘occurring with little or no warning and often cause excessive injuries far surpassing the national response capacities’. Based on the panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2015, a predictive method was developed to predict the human impact of the disaster (fatality, injured, homeless) with less than 3% of errors. The geographical dispersion of the disasters includes every country where the data were available and cross-examined from various humanitarian sources. The records were then filtered into 4252 records of the disasters where the five predictive variables (disaster type, HDI, DRI, population, and population density) were clearly stated. The procedure was designed based on a combination of pattern recognition techniques and rule-based clustering for prediction and discrimination analysis to validate the results further. The result indicates that there is a relationship between the disaster human impact and the five socio-economic characteristics of the affected country mentioned above. As a result, a framework was put forward, which could predict the disaster’s human impact based on their severity rank in the early hours of disaster strike. The predictions in this model were outlined in two worst and best-case scenarios, which respectively inform the lower range and higher range of the prediction. A necessity to develop the predictive framework can be highlighted by noticing that despite the existing research in literature, a framework for predicting the human impact and estimating the needs at the time of the disaster is yet to be developed. This can further be used to allocate the resources at the response phase of the disaster where the data is scarce.

Keywords: disaster management, natural disaster, pattern recognition, prediction

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Authors: Bingquan Shen

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Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline.

Keywords: convolutional neural networks, object classification, pose normalization, viewpoint invariant

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Authors: Saeid Gharechelou, Ryutaro Tateishi

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Earthquake is an inevitable catastrophic natural disaster. The damages of buildings and man-made structures, where most of the human activities occur are the major cause of casualties from earthquakes. A comparison of optical and SAR data is presented in the case of Kathmandu valley which was hardly shaken by 2015-Nepal Earthquake. Though many existing researchers have conducted optical data based estimated or suggested combined use of optical and SAR data for improved accuracy, however finding cloud-free optical images when urgently needed are not assured. Therefore, this research is specializd in developing SAR based technique with the target of rapid and accurate geospatial reporting. Should considers that limited time available in post-disaster situation offering quick computation exclusively based on two pairs of pre-seismic and co-seismic single look complex (SLC) images. The InSAR coherence pre-seismic, co-seismic and post-seismic was used to detect the change in damaged area. In addition, the ground truth data from field applied to optical data by random forest classification for detection of damaged area. The ground truth data collected in the field were used to assess the accuracy of supervised classification approach. Though a higher accuracy obtained from the optical data then integration by optical-SAR data. Limitation of cloud-free images when urgently needed for earthquak evevent are and is not assured, thus further research on improving the SAR based damage detection is suggested. Availability of very accurate damage information is expected for channelling the rescue and emergency operations. It is expected that the quick reporting of the post-disaster damage situation quantified by the rapid earthquake assessment should assist in channeling the rescue and emergency operations, and in informing the public about the scale of damage.

Keywords: Sentinel-1A data, Landsat-8, earthquake damage, InSAR, rapid damage monitoring, 2015-Nepal earthquake

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Authors: Y. Tiamaz, N. Souissi

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The purpose of this paper is to present a classification of Lean models which aims to capture all the concepts related to this approach and thus facilitate its implementation. This classification allows the identification of the most relevant models according to several dimensions. From this perspective, we present a review and an analysis of Lean models literature and we propose dimensions for the classification of the current proposals while respecting among others the axes of the Lean approach, the maturity of the models as well as their application domains. This classification allowed us to conclude that researchers essentially consider the Lean approach as a toolbox also they design their models to solve problems related to a specific environment. Since Lean approach is no longer intended only for the automotive sector where it was invented, but to all fields (IT, Hospital, ...), we consider that this approach requires a generic model that is capable of being implemented in all areas.

Keywords: lean approach, lean models, classification, dimensions, holistic view

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Authors: S. Pullteap, M. Pathomsuriyaporn

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In this article, a study of employee’s opinion to the factors that affect to the flood preventive and the corrective action plan in an electronic industry at the Sharp Manufacturing (Thailand) Co., Ltd. has been investigated. The surveys data of 175 workers and supervisors have, however, been selected for data analysis. The results is shown that the employees emphasize about the needs in a subsidy at the time of disaster at high levels of 77.8%, as the plan focusing on flood prevention of the rehabilitation equipment is valued at the intermediate level, which is 79.8%. Demonstration of the hypothesis has found that the different education levels has thus been affected to the needs factor at the flood disaster time. Moreover, most respondents give priority to flood disaster risk management factor. Consequently, we found that the flood prevention plan is valued at high level, especially on information monitoring, which is 93.4% for the supervisor item. The respondents largely assume that the flood will have impacts on the industry, up to 80%, thus to focus on flood management plans is enormous.

Keywords: flood prevention plan, flood event, electronic industrial plant, disaster, risk management

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Authors: K. W. A. M. Kokila, Matsui Kenichi

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Sri Lanka’s disaster management policy aims to protect lives and developments in disaster affected areas by effectively using resources for disaster risk reduction, emergency management, and community awareness. However, funding for these action programs has posed a serious challenge to the country’s economy. This paper examines the extent to which private-public partnerships (PPPs) can facilitate and expedite disaster management works. In particular, it discusses the results of the questionnaire survey among policymakers, government administrators, NGOs, and private businesses. This questionnaire was conducted in 2017. All respondents were selected based on their experience in PPP projects in the past. The survey focused on clarifying the effectiveness of past PPP projects as well as their efficiency and transparency. The respondents also provided their own opinions and suggestions to improve the future PPP projects in Sri Lanka. The questionnaire was distributed to fifteen persons. The results show that almost all respondents think that PPP projects are beneficial and important for future disaster risk management in Sri Lanka. The respondents, however, showed some reservation about effectiveness and transparency of the PPP process. This paper also discusses the results on the respondents’ perceptions about their capacity regarding human resources and management. This paper, overall, sheds light on technological, financial and human resource management practices in developed countries as well as policy and legislation provisions regarding PPP projects.

Keywords: disaster management, policy, private public partnership, projects

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Authors: Sweta Kandari

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Predominantly, the conventional role of an architect is to design and construct. However, in a post-disaster scenario, the prevalent role expands and includes many other responsibilities. Agencies collaborating in post-disaster reconstruction face the challenge of building back quickly while requiring them to listen, reflect, develop and deliver as per the needs and requirements of the people. The question of the role of an architect has been extensively discussed in the reconstruction field. Discourses about the role of an architect in post-disaster scenario revolve around the ignorance by the profession, their professional abilities and inabilities. Within this domain, this paper aims at analyzing and recognizing the roles, responsibilities, scope, limitations, skillsets applied and required by an architect while working in a post-disaster situation. Four projects rebuilt after the 2001 Bhuj earthquake in Gujarat, India were examined for this research. Based on the analysis of the case study, areas of intervention of an architect in the various stages of rebuilding were identified. It was reinforced that within the areas of intervention identified, there is a vast gap between the prescribed, the prevalent notion and the performed responsibilities of an architect. This paper brings forth the specific gaps in the rebuilding process while exploring and understanding the relationship between various stakeholders that influence the role of an architect.

Keywords: rebuilding, role of an architect, Bhuj, post-disaster

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Authors: Majda Al Salti

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Tourism industry is considered globally as one of the leading industries due to its noticeable contribution to countries' gross domestic product (GDP) and job creation. However, tourism is vulnerable to crisis and disaster that requires its preparedness. With its limited capabilities, there is a need to improve links and the understanding between the tourism industry and the emergency services, thus facilitating future emergency response to any potential incident. This study aims to develop the concept of an integrated emergency management system for the tourism industry. The study used face-to-face semi-structured interviews to evaluate the level of crisis and disaster preparedness of the tourism industry in Oman. The findings suggested that there is a lack of understanding of crisis and disaster management, and hence preparedness level among Oman Tourism Authorities appears to be under-expectation. Therefore, a clear need for tourism sector inter- and intra-integration and collaboration is important in the pre-disaster stage. The need for such integrations can help the tourism industry in Oman to prepare for future incidents as well as identifying its requirements in time of crisis for effective response.

Keywords: tourism, emergency services, crisis, disaster

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Authors: Mohammad H. Yarmohamadian, Reza Safdari, Nahid Tavakoli

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In recent years, many countries have suffered various natural disasters. Disaster response continues to face the challenges in health care sector in all countries. Information and communication management is a significant challenge in disaster scene. During the last decades, rapid advances in information technology have led to manage information effectively and improve communication in health care setting. Information technology is a vital solution for effective response to disasters and emergencies so that if an efficient ICT-based health information system is available, it will be highly valuable in such situation. Of that, mobile technology represents a nearly computing technology infrastructure that is accessible, convenient, inexpensive and easy to use. Most projects have not yet reached the deployment stage, but evaluation exercises show that mHealth should allow faster processing and transport of patients, improved accuracy of triage and better monitoring of unattended patients at a disaster scene. Since there is a high prevalence of cell phones among world population, it is expected the health care providers and managers to take measures for applying this technology for improvement patient safety and public health in disasters. At present there are challenges in the utilization of mhealth in disasters such as lack of structural and financial issues in our country. In this paper we will discuss about benefits and challenges of mhealth technology in disaster setting considering connectivity, usability, intelligibility, communication and teaching for implementing this technology for disaster response.

Keywords: information technology, mhealth, disaster, effective response

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Authors: Priyatma Singh, Manpreet Kaur

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Climate change is progressively being identified as a global crisis and this has immediate repercussions for Fiji Islands due to its geographical location being prone to natural disasters. In the Pacific, it is common to find significant differences between men and women, in terms of their roles and responsibilities. In the pursuit of prudent preparedness before disasters, Fijian women’s engagement is constrained due to socially constructed roles and expectation of women here in Fiji. This vulnerability is aggravated by viewing women as victims, rather than as key people who have vital information of their society, economy, and environment, as well as useful skills, which, when recognized and used, can be effective in disaster risk reduction. The focus of this study on disaster management is to outline ways in which Fijian women can be actively engaged in disaster risk management, articulating in decision-making, negating the perceived ideology of women’s constricted roles in Fiji and unveiling social constraints that limit women’s access to practical disaster management strategic plan. This paper outlines the importance of gender mainstreaming in disaster risk reduction and the ways of mainstreaming gender based on a literature review. It analyses theoretical study of academic literature as well as papers and reports produced by various national and international institutions and explores ways to better inform and engage women for climate change per ser disaster management in Fiji. The empowerment of women is believed to be a critical element in constructing disaster resilience as women are often considered to be the designers of community resilience at the local level. Gender mainstreaming as a way of bringing a gender perspective into climate related disasters can be applied to distinguish the varying needs and capacities of women, and integrate them into climate change adaptation strategies. This study will advocate women articulation in disaster risk management, thus giving equal standing to females in Fiji and also identify the gaps and inform national and local Disaster Risk Management authorities to implement processes that enhance gender equality and women’s empowerment towards a more equitable and effective disaster practice.

Keywords: disaster risk management, climate change, gender mainstreaming, women empowerment

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Authors: Ida Ngurah

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Indonesia is one of the most prone disaster countries, which has earthquake, tsunami or high wave, flood and landslide as well as volcano eruption and drought. Disaster risk reduction has been developing extensively and comprehensively, particularly after tsunami hit in 2004. Yet, saving people live including children and youth from disaster risk is still far from succeed. Poor management of environment, poor development of policy and high level of corruption has become challenges for Indonesia to save its people from disaster impact. Indonesia is struggling to ensure its future best investment, children and youth to have better protection when disaster strike in school hours and have basic knowledge on disaster risk reduction. The program of safe school is being initiated and developed by Plan Indonesia since 2010, yet this effort still needs to be elaborated. This paper is reviewing sporadic safe school programs that have been implemented or currently being implemented Plan Indonesia in few areas of Indonesia, including both rural and urban setting. Methods used are in-depth interview with dedicated person for the program from Plan Indonesia and its implementing patners and analysis of project documents. The review includes program’s goal and objectives, implementation activity, result and achievement as well as its monitoring and evaluation scheme. Moreover, paper will be showing challenges, lesson learned and best practices of the program. Eventually, paper will come up with recommendation for strategy for better implementation of safe school program in Indonesia.

Keywords: disaster impact, safe school, programs, children, youth

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Authors: Moein Izadi, Ali Mohammadzadeh

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Detection of damaged parts of roads after earthquake is essential for coordinating rescuers. In this study, an approach is presented for the semi-automatic detection of damaged roads in a city using pre-event vector maps and both pre- and post-earthquake QuickBird satellite images. Damage is defined in this study as the debris of damaged buildings adjacent to the roads. Some spectral and texture features are considered for SVM classification step to detect damages. Finally, the proposed method is tested on QuickBird pan-sharpened images from the Bam City earthquake and the results show that an overall accuracy of 81% and a kappa coefficient of 0.71 are achieved for the damage detection. The obtained results indicate the efficiency and accuracy of the proposed approach.

Keywords: SVM classifier, disaster management, road damage detection, quickBird images

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Authors: Shuo Liu

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In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text.

Keywords: Chinese natural language processing, text classification, abstract extraction, graph attention network

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Authors: K. S. Parlak, E. Turan

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The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

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Authors: M. C. Teng, M. C. Ke, C. Y. Yang, S. S. Ke

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There are more than one thousand times a year of felt earthquakes in Taiwan. Because earthquakes are disaster threats to urban infrastructure, they often disrupt infrastructure services. For example, the highway system is very important to transportation infrastructure; however, it is vulnerable to earthquakes and typhoons in Taiwan. When a highway system is damaged by disaster, it will create a major impact on post-disaster communications and emergency relief and affect disaster relief works. In a study case on September 18th, 2022, the Taitung Chihshang earthquake, with a magnitude of 6.8 on the Richter scale with a depth of 7 km, caused one death; 171 people were injured and had a significant urban infrastructure impact. Hualien and Taitung areas have a large number of surface ruptures, road disruptions due to the collapses, over ten cases of bridges failure or closed, partial railroad section service shutdown, building collapses, and casualties. Taitung Chihshang earthquake, the peak ground acceleration is 585 gal (cm/s²), and the seismic intensity is Level 6 Upper(6+)in Chishang, Taitung County. After the earthquakes, we conducted on-site disaster investigation works in the disaster area; the disaster investigation works included a public and private building survey, a transportation facility survey, a total of ten damaged bridges, and one railroad station damaged were investigated in this investigation. The results showed that the affected locations were mainly concentrated along the Chihshang fault and the Yuli fault in the Huatung Longitudinal Valley. We recorded and described the impact and assessed its influence region in terms of its susceptibility to and the consequences of earthquake attacks. In addition, a lesson is learned from this study regarding the key issues after the Taitung Chihshang earthquake.

Keywords: earthquake, infrastructure, disaster investigation, lesson learned

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Authors: M. Hijazi, A. Zeki, A. Ismail

Abstract:

An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia.

Keywords: Arabic text classification, Arabic WordNet, bag of words, conceptual representation, semantic relations

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Authors: Bhuyain Mobarok Hossain

Abstract:

Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Zulkiflee Abd Latif, Sitinor Atikah Nordin, Alawi Sulaiman

Abstract:

Different tree species possess different and various benefits. Arenga Pinnata tree species own several potential uses that is valuable for the economy and the country. Mapping vegetation using remote sensing technique involves various process, techniques and consideration. Using satellite imagery, this method enables the access of inaccessible area and with the availability of near infra-red band; it is useful in vegetation analysis, especially in identifying tree species. Pixel-based and object-based classification technique is used as a method in this study. Pixel-based classification technique used in this study divided into unsupervised and supervised classification. Object based classification technique becomes more popular another alternative method in classification process. Using spectral, texture, color and other information, to classify the target make object-based classification is a promising technique for classification. Classification of Arenga Pinnata trees is overlaid with elevation, slope and aspect, soil and river data and several other data to give information regarding the tree character and living environment. This paper will present the utilization of remote sensing technique in order to map Arenga Pinnata tree species

Keywords: Arenga Pinnata, pixel-based classification, object-based classification, remote sensing

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Authors: Ghada S. Moussa

Abstract:

With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management. This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications.

Keywords: appearance attributes, geometric attributes, support vector machine, vehicle classification

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