Search results for: natural disaster forecasting

Authors: Laura Rodríguez Amaya

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The impact of natural disasters on food security can be devastating, especially in rural settings where livelihoods are closely tied to their productive assets. In hazards studies, absorptive capacity is seen as a threshold that impacts the degree of people’s recovery after a natural disaster. Increasing our understanding of households’ capacity to absorb natural disaster shocks can provide the international community with viable measurements for assessing at-risk communities’ resilience to food insecurities. The purpose of this study is to identify the most important factors in determining a household’s capacity to absorb the impact of a natural disaster. This is an empirical study conducted in six communities in Costa Rica affected by earthquakes. The Earthquake Impact Index was developed for the selection of the communities in this study. The households coded as total loss in the selected communities constituted the sampling frame from which the sample population was drawn. Because of the study area geographically dispersion over a large surface, the stratified clustered sampling hybrid technique was selected. Of the 302 households identified as total loss in the six communities, a total of 126 households were surveyed, constituting 42 percent of the sampling frame. A list of indicators compiled based on theoretical and exploratory grounds for the absorptive capacity construct served to guide the survey development. These indicators were included in the following variables: (1) use of informal safety nets, (2) Coping Strategy, (3) Physical Connectivity, and (4) Infrastructure Damage. A multivariate data analysis was conducted using Statistical Package for Social Sciences (SPSS). The results show that informal safety nets such as family and friends assistance exerted the greatest influence on the ability of households to absorb the impact of earthquakes. In conclusion, communities that experienced the highest environmental impact and human loss got disconnected from the social networks needed to absorb the shock’s impact. This resulted in higher levels of household food insecurity.

Keywords: absorptive capacity, earthquake, food security, rural

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Authors: Daniel Fulus Fom, Gau Patrick Damulak

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In this study, the Autoregressive Fractional Integrated Moving Average (ARFIMA) and Jordan Recurrent Neural Network (JRNN) models were employed to model the forecasting performance of the daily turbidity flow of White Clay Creek (WCC). The two methods were applied to the log difference series of the daily turbidity flow series of WCC. The measurements of error employed to investigate the forecasting performance of the ARFIMA and JRNN models are the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The outcome of the investigation revealed that the forecasting performance of the JRNN technique is better than the forecasting performance of the ARFIMA technique in the mean square error sense. The results of the ARFIMA and JRNN models were obtained by the simulation of the models using MATLAB version 8.03. The significance of using the log difference series rather than the difference series is that the log difference series stabilizes the turbidity flow series than the difference series on the ARFIMA and JRNN.

Keywords: auto regressive, mean absolute error, neural network, root square mean error

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Authors: Anita Setianingrum, Oki S. Jaya, Zuherman Rustam

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Forecasting stock price is a challenging task due to the complex time series of the data. The complexity arises from many variables that affect the stock market. Many time series models have been proposed before, but those previous models still have some problems: 1) put the subjectivity of choosing the technical indicators, and 2) rely upon some assumptions about the variables, so it is limited to be applied to all datasets. Therefore, this paper studied a novel Adaptive Neuro-Fuzzy Inference System (ANFIS) time series model based on Support Vector Regression (SVR) for forecasting the stock market. In order to evaluate the performance of proposed models, stock market transaction data of TAIEX and HIS from January to December 2015 is collected as experimental datasets. As a result, the method has outperformed its counterparts in terms of accuracy.

Keywords: ANFIS, fuzzy time series, stock forecasting, SVR

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Authors: Byron Malaela Sotiata Seiuli

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When a disaster strike, the resultant impact and devastation forces many people, particularly those directly affected, to re-examine the core dimensions of life that do not come from other life events. The way people respond to and try give meaning to their experiences resultant from the ruptures of trauma remains vital in grief recovery. On 29 October 2009, an earthquake of 8.3 magnitudes generated a galulolo (tsunami) wave that destroyed parts of American Samoa, Tonga and Samoa (previously Western Samoa). Aside from the physical and natural devastation, many people lost their lives and their livelihood. For health professionals who were called upon to provide psychosocial support, this calamity provided an ideal setting to examine and explore how those directly impacted recovered from the calamity. The experiences of a Samoan couple, Fia and Ola, becomes the key focus of this article, one that situates their mourning patterns and recovery journey in the context of Samoan culture. Examining grief from this perspective creates a cultural space to extend indigenous understanding on the complexities of grieving and customarily responses of Samoan people, like this couple, to disaster recovery.

Keywords: Fa'asamoa, galulolo, tsunami disaster, trauma and grief recovery, pacific psychology

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Authors: U. V. Okpala

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Flood is an overflow of water that submerges or ‘drowns’ land. In developing countries it occurs as a result of blocking of natural and man-made drainages and poor maintenance of water dams/reservoirs which seldom give way after persistent heavy down pours. In coastal lowlands and swamp lands, flooding is aided mainly by blocked channels and indiscriminate sand fling of coastal swamp areas and natural drainage channel for urban development/constructions. In this paper, the causes of flood and possible scientific, technological, political, economic and social impacts of flood disaster on the environment a case study of Anambra State have been studied. Often times flooding is caused by climate change, especially in the developed economy where scientific mitigating options are highly employed. Researchers have identified Green Houses Gases (GHG) as the cause of global climate change. The recent flood disaster in Anambra State which caused physical damage to structures, social dislocation, contamination of clean drinking water, spread of water-borne diseases, shortage of crops and food supplies, death of non-tolerant tree species, disruption in transportation system, serious economic loss and psychological trauma is a function of climate change. There is need to encourage generation of renewable energy sources, use of less carbon intensive fuels and other energy efficient sources. Carbon capture/sequestration, proper management of our drainage systems and good maintenance of our dams are good option towards saving the environment.

Keywords: flooding, climate change, carbon capture, energy systems

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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: S. M. Amin Hosseini, Oriol Pons, Albert de la Fuente

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After natural disasters, displaced people (DP) require important numbers of housing units, which have to be erected quickly due to emergency pressures. These tight timeframes can cause the multiplication of the environmental construction impacts. These negative impacts worsen the already high energy consumption and pollution caused by the building sector. Indeed, post-disaster housing, which is often carried out without pre-planning, usually causes high negative environmental impacts, besides other economic and social impacts. Therefore, it is necessary to establish a suitable strategy to deal with this problem which also takes into account the instability of its causes, like changing ratio between rural and urban population. To this end, this study aims to present a model that assists decision-makers to choose the most suitable building technology for post-disaster housing units. This model focuses on the alternatives sustainability and fulfillment of the stakeholders’ satisfactions. Four building technologies have been analyzed to determine the most sustainability technology and to validate the presented model. In 2003, Bam earthquake DP had their temporary housing units (THUs) built using these four technologies: autoclaved aerated concrete blocks (AAC), concrete masonry unit (CMU), pressed reeds panel (PR), and 3D sandwich panel (3D). The results of this analysis confirm that PR and CMU obtain the highest sustainability indexes. However, the second life scenario of THUs could have considerable impacts on the results.

Keywords: sustainability, post-disaster temporary housing, integrated value model for sustainability assessment, life cycle assessment

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Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

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As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

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Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: air quality prediction, deep learning algorithms, time series forecasting, look-back window

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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: Aswathi Thrivikraman, S. Advaith

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The study is aimed at designing encoders for deciphering intricacies in time series data by redescribing the dynamics operating on a lower-dimensional manifold. A semi-supervised LSTM autoencoder is devised and investigated to see if the latent representation of the time series data can better forecast the data. End-to-end training of the LSTM autoencoder, together with another LSTM network that is connected to the latent space, forces the hidden states of the encoder to represent the most meaningful latent variables relevant for forecasting. Furthermore, the study compares the predictions with those of a traditional seq2seq model.

Keywords: LSTM, autoencoder, forecasting, seq2seq model

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Authors: S M Asger Ali, Duane A. Gill

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During crisis moments like a natural disaster, people tend to rely on the mass media to get up-to-date information and stay informed. However, when media are covering crisis news, they may lose some objectivity, and rather than providing balanced news coverage, media may become critical towards the government and private sectors for their participation in disaster response and recovery processes. This paper investigated the print media coverage of Hurricane Harvey and utilized data from three newspapers: the New York Times (online), the Wall Street Journal (online), and the Houston Chronicle. By examining the media's use of descriptors, quotes, wording, and images, this research explored how media coverage framed government and private sectors for their role in Harvey's response and recovery. Findings revealed that the human-interest frame received the most media attention, and the morality frame received less attention. Regarding tone, this study found that the media's overall tone for government response was neutral. However, the tone for the federal government was slightly negative, while the tone for city and state level of government was slightly positive. By examining the media's tone and frame, this research contributes to the literature on risk communication, mass media, and disaster studies.

Keywords: hurricane Harvey, mass media, risk communication, disaster response, media framing, crisis news coverage

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Authors: Mary Holstein

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Objective: The aim of this study was to evaluate nurse leader confidence in emergency management and disaster preparedness in the state of Texas. My project was a replication study of a survey conducted in 2022 by Reedy et al, for members of the Northwest Organization for Nurse Leaders (NONL). Background: In 2022, the American Association of Colleges of Nursing (AACN) approved new essentials for academic nursing education programs to demonstrate competencies in disaster management, yet no integration of such information into nursing curriculum had been reported in the literature. Research replicated by members of the Texas Organization for Nursing Leadership suggested significant gaps in nurse leader confidence across roles and in structured education that prepares nurse leaders across the spectrum of experience to lead in a crisis. Methods: An exploratory, cross-sectional survey used a sample of 86 RNs who were members of TONL. Results: Results replicated comparable results with significant variance in nurse leader confidence across roles, experience, and previous disaster-related education. Positive associations regarding nurse leaders' confidence in managing disasters were obvious with more advanced positions, further education, and mandatory training. Conclusions: Nursing leaders in Texas lack mandatory and structured education to prepare for emergency and disaster management. The call for mandatory emergency management training and disaster preparedness for nurse leaders remains unmet.

Keywords: confidence, disaster, education, emergency

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Authors: K. Vallianou, T. Alexopoulos, V. Plaka, M. K. Seleventi, V. Skanavis, C. Skanavis

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The present research addresses the role of place attachment and emotions in community resiliency and recovery within the context of a disaster. Natural disasters represent a disruption in the normal functioning of a community, leading to a general feeling of disorientation. This study draws on the trauma caused by a natural hazard such as a forest fire. The changes of the sense of togetherness are being assessed. Finally this research determines how the place attachment of the inhabitants was affected during the reorientation process of the community. The case study area is Mati, a small coastal town in eastern Attica, Greece. The fire broke out on July 23^rd, 2018. A quantitative research was conducted through questionnaires via phone interviews, one year after the disaster, to address community resiliency in the long-run. The sample was composed of 159 participants from the rural community of Mati plus 120 coming from Skyros Island that was used as a control group. Inhabitants were prompted to answer items gauging their emotions related to the event, group identification and emotional significance of their community, and place attachment before and a year after the fire took place. Importantly, the community recovery and reorientation were examined within the context of a relative absence of government backing and official support. Emotions related to the event were aggregated into 4 clusters related to: activation/vigilance, distress/disorientation, indignation, and helplessness. The findings revealed a decrease in the level of place attachment in the impacted area of Mati as compared to the control group of Skyros Island. Importantly, initial distress caused by the fire prompted the residents to identify more with their community and to report more positive feelings toward their community. Moreover, a mediation analysis indicated that the positive effect of community cohesion on place attachment one year after the disaster was mediated by the positive feelings toward the community. Finally, place attachment contributes to enhanced optimism and a more positive perspective concerning Mati’s future prospects. Despite an insufficient state support to this affected area, the findings suggest an important role of emotions and place attachment during the process of recovery. Implications concerning the role of emotions and social dynamics in meshing place attachment during the disaster recovery process as well as community resiliency are discussed.

Keywords: community resilience, natural disasters, place attachment, wildfire

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Authors: Flavio Cordeiro Fontanella, Asla Medeiros e Sá, Moacyr Alvim Horta Barbosa da Silva

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In the present work, we analyse the performance of football results forecasting models. In order to do so, we have performed the data collection from eight different forecasting models during the 2021 Brazilian football season. First, we guide the analysis through visual representations of the data, designed to highlight the most prominent features and enhance the interpretation of differences and similarities between the models. We propose using a 2-simplex triangle to investigate visual patterns from the results forecasting models. Next, we compute the expected points for every team playing in the championship and compare them to the final league standings, revealing interesting contrasts between actual to expected performances. Then, we evaluate forecasts’ accuracy using the Ranked Probability Score (RPS); models comparison accounts for tiny scale differences that may become consistent in time. Finally, we observe that the Wisdom of Crowds principle can be appropriately applied in the context, driving into a discussion of results forecasts usage in practice. This paper’s primary goal is to encourage football forecasts’ performance discussion. We hope to accomplish it by presenting appropriate criteria and easy-to-understand visual representations that can point out the relevant factors of the subject.

Keywords: accuracy evaluation, Brazilian championship, football results forecasts, forecasting models, visual analysis

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Authors: Jieh-Jiuh Wang

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Disasters happen frequently due to the global climate changes today. The cultural heritage conservation should be considered from the perspectives of surrounding environments and large-scale disasters. Most current thoughts about the disaster prevention of cultural heritages in Taiwan are single-point thoughts emphasizing firefighting, decay prevention, and construction reinforcement and ignoring the whole concept of the environment. The traditional conservation cannot defend against more and more tremendous and frequent natural disasters caused by climate changes. More and more cultural heritages are confronting the high risk of disasters. This study adopts the perspective of risk identification and takes flood as the main disaster category. It analyzes the amount and categories of cultural heritages that might suffer from disasters with the geographic information system integrating the latest flooding potential data from National Fire Agency and Water Resources Agency and the basic data of cultural heritages. It examines the actual risk of cultural heritages confronting floods and serves as the accordance for future considerations of risk measures and preparation for reducing disasters. The result of the study finds the positive relationship between the disaster affected situation of national cultural heritages and the rainfall intensity. The order of impacted level by floods is historical buildings, historical sites indicated by municipalities and counties, and national historical sites and relics. However, traditional settlements and cultural landscapes are not impacted. It might be related to the taboo space in the traditional culture of site selection (concepts of disaster avoidance). As for the regional distribution on the other hand, cultural heritages in central and northern Taiwan suffer from more shocking floods, while the heritages in northern and eastern Taiwan suffer from more serious flooding depth.

Keywords: cultural heritage, flood, preventive conservation, risk management

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Authors: N. Sopipan

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In this paper, we study of rainfall time series of weather stations in Nakhon Ratchasima province in Thailand using various statistical methods enabled to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. ARIMA and Holt-Winter models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Nakhon Ratchasima province. We found the best perform for forecasting is ARIMA(1,0,1)(1,0,1)12.

Keywords: ARIMA Models, exponential smoothing, Holt-Winter model

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Authors: Reinhold Kosfeld, Andreas Gohs

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In the reduction of CO₂ emissions, the transition to environmentally friendly transport modes has a high significance. In Germany, the climate protection programme 2030 includes various measures for promoting electromobility. Although electric cars at present hold a market share of just over one percent, its stock more than doubled in the past two years. Special measures like tax incentives and a buyer’s premium have been put in place to promote the shift towards electric cars and boost their diffusion. Knowledge of the future expansion of electric cars is required for planning purposes and adaptation measures. With a view of these objectives, we particularly investigate the effect of spatial spillovers on forecasting performance. For this purpose, time series econometrics and panel econometric models are designed for pure electric cars and hybrid cars for Germany. Regional forecasting models with spatial interactions are consistently estimated by using spatial econometric techniques. Regional data on the stocks of electric cars and their determinants at the district level (NUTS 3 regions) are available from the Federal Motor Transport Authority (Kraftfahrt-Bundesamt) for the period 2017 - 2019. A comparative examination of aggregated regional and national predictions provides quantitative information on accuracy gains by allowing for spatial spillovers in forecasting electric mobility.

Keywords: electric mobility, forecasting market diffusion, regional panel data model, spatial interaction

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Authors: Sunho Seo, Younghwan Shin, Jong-Hong Park, Sooeun Song, Junsung Kim, Jusik Yun, Yongkyun Kim, Jong-Moon Chung

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In order to effectively cope with large and complex disasters, disaster response training is needed. Recently, disaster response training led by the ROK (Republic of Korea) government is being implemented through a 4 year R&D project, which has several similar functions as the HSEEP (Homeland Security Exercise and Evaluation Program) of the United States, but also has several different features as well. Due to the unpredictiveness and diversity of disasters, existing training methods have many limitations in providing experience in the efficient use of disaster incident response and recovery resources. Always, the challenge is to be as efficient and effective as possible using the limited human and material/physical resources available based on the given time and environmental circumstances. To enable repeated training under diverse scenarios, an AR (Augmented Reality) and VR (Virtual Reality) combined simulator is under development. Unlike existing disaster response training, simulator based training (that allows remote login simultaneous multi-user training) enables freedom from limitations in time and space constraints, and can be repeatedly trained with different combinations of functions and disaster situations. There are related systems such as ADMS (Advanced Disaster Management Simulator) developed by ETC simulation and HLS2 (Homeland Security Simulation System) developed by ELBIT system. However, the ROK government needs a simulator custom made to the country's environment and disaster types, and also combines the latest information and communication technologies, which include AR, VR, and MPEG-DASH (Moving Picture Experts Group - Dynamic Adaptive Streaming over HTTP) technology. In this paper, a new disaster response training simulator is proposed to overcome the limitation of existing training systems, and adapted to actual disaster situations in the ROK, where several technical features are described.

Keywords: augmented reality, emergency response training simulator, MPEG-DASH, virtual reality

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Authors: Peng Liu, Chyng Wen Tee, Xiaofei Xu

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This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike.

Keywords: multi-period portfolio optimization, look-ahead constrained optimization, machine learning, sequential decision making

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Authors: Kunto Wibowo Agung Prodjonoto

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Occasionally, opportunities occur not due to chances but threats. This, however, is often not realized because a greater threat is perceived to be anything that threatens, endangers, or harms, resulting in bad impacts that are also part of the risk and consequence. As a result, more focus tends to direct towards the bad impacts. Risk, in this case, shall be seen rather as something challenging, which can turn to be an opportunity to tackle an obstacle. Therefore, it does not seem exaggerating if later, risk can be considered as a challenge that presents an opportunity. So as in the context of the threat of natural disasters which gives an idea that opportunities exist. Nature referred to in a fashion as 'natural disasters' captured an expression to picture the 'threats' aspect, which instructively implying a chance of opportunity. This is quite logical, as SWOT (strengths, weaknesses, opportunities, threats) analysis can evaluate the situation at hand related to the analysis of various factors in formulating strategies to deal with natural disaster situations. The analytical method created by Albert Humphrey is indeed not an analytical tool to provide solutions, but certainly 'opportunities and challenges' are discussed therein on a vertical line, where opportunities are posited on the positive axis, and threats are posed on the negative axis. Observing this dynamism, the challenges and threats of disasters are having opportunity relevance to moralizing opportunities, that by quality poses universalism populist characteristics, universalism characteristics, and regional characteristics. Here, universalism appears as an opportunity domain underneath the threats and challenges of natural disasters.

Keywords: universality, opportunities, threats, challenges of natural disasters

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Authors: Liza Ireni-Saban

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The 6.6 magnitude quake event that occurred in 2003 (Bam, Iran) made it impossible for the Iranian government to handle disaster relief efforts domestically. In this extreme event, the Iranian government reached out to the international community, and this created a momentum that had to be carried out by trust-building efforts on all sides, often termed ‘Disaster Diplomacy’. Indeed, the circumstances were even more critical when one considers the increasing political and economic isolation of Iran within the international community. The potential for transformative political space to be opened by disaster has been recognized by dominant international political actors. Despite the fact that Bam 2003 post-disaster relief efforts did not catalyze any diplomatic activities on all sides, it is suggested that few international aid agencies have successfully used disaster recovery to enhance their popular legitimacy and reputation among the international community. In terms of disaster diplomacy, an actor’s reputational capital may affect his ability to build coalitions and alliances to achieve international political ends, to negotiate and build understanding and trust with foreign publics. This study suggests that the post-disaster setting may benefit from using the ecology of games framework to evaluate the role of bridging actors and mediators in facilitating collaborative governance networks. Recent developments in network theory and analysis provide means of structural embeddedness to explore how reputational capital can be built through brokerage roles of actors engaged in a disaster management network. This paper then aims to structure the relations among actors that participated in the post-disaster relief efforts in the 2003 Bam earthquake (Iran) in order to assess under which conditions actors may be strategically utilized to serve as mediating organizations for future disaster events experienced by isolated nations or nations in conflict. The results indicate the strategic use of reputational capital by the Iranian Ministry of Foreign Affairs as key broker to build a successful coordinative system for reducing disaster vulnerabilities. International aid agencies rarely played brokerage roles to coordinate peripheral actors. U.S. foreign assistance (USAID), despite coordination capacities, was prevented from serving brokerage roles in the system.

Keywords: coordination, disaster diplomacy, international aid organizations, Iran

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Herawanti Kumalasari, Raldi Hendro Koestoer, Hayati Sari Hasibuan

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Geographically, Indonesia is located in the arc of volcanoes that cause disaster prone one of them is landslide disaster. One of the causes of the landslide is the conversion of land from forest to agricultural land in upland areas and river border that has a steep slope. The study area is located in the highlands with fertile soil conditions, so most of the land is used as agricultural land and plantations. Land use transfer also occurs around the geothermal field in Pangalengan District, West Java Province which will threaten the sustainability of geothermal energy utilization and the safety of the community. The purpose of this research is to arrange the concept of spatial pattern arrangement in the geothermal area based on disaster mitigation. This research method using superimpose analysis. Superimpose analysis to know the basic physical condition of the planned area through the overlay of disaster risk map with the map of the plan of spatial plan pattern of Bandung Regency Spatial Plan. The results of the analysis will then be analyzed spatially. The results have shown that most of the study areas were at moderate risk level. Planning of spatial pattern of existing study area has not fully considering the spread of disaster risk that there are settlement area and the agricultural area which is in high landslide risk area. The concept of the arrangement of the spatial pattern of the study area will use zoning system which is divided into three zones namely core zone, buffer zone and development zone.

Keywords: spatial planning, geothermal, disaster risk, zoning

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Authors: S. B. D. Samarasinghe, Malini Herath

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Participatory planning is a very important process for decision making and choosing the best alternative options for community welfare, development of the society and its interactions among community and professionals. People’s involvement is considered as the key guidance in participatory planning. Presently, Participatory planning is being used in many fields. It's not only limited to planning but also to disaster management, poverty, housing, etc. In the past, Disaster management practice was a top-down approach, but it raised many issues as it was converted to a bottom-up approach. There are several approaches that can aid disaster management. Community-Based Disaster Risk Management (CBDRM) is a very successful participatory approach to risk management that is often successfully applied by other disaster-prone countries. In the local context, CBDRM has been applied to prevent Diseases as well as to prevent disasters such as landslides, tsunamis and floods. From three years before, Sri Lanka has initiated the CBDRM approach to minimize landslide vulnerability. Hence, this study mainly focuses on the impact of CBDRM approaches on landslide hazards. Also to identify their successes and failures from both implementing parties and community. This research is carried out based on a qualitative method combined with a descriptive research approach. A successful framework was prepared via a literature review. Case studies were selected considering landslide CBDRM programs which were implemented by Disaster Management Center and National Building Research Organization in Badulla. Their processes were evaluated. Data collection is done through interviews and informal discussions. Then their ideas were quantified by using the Relative Effectiveness index. The resulting numerical value was used to rank the program effectiveness and their success, failures and impacting factors. Results show that there are several failures among implementing parties and the community. Overcoming those factors can make way for better conduction of future CBDRM programs.

Keywords: community-based disaster risk management, disaster management, preparedness, landslide

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Authors: Kashif Hassan, M.A. Ahanger

Abstract:

Floods have become more frequent and severe due to effects of global climate change and human alterations of the natural environment. Flood prediction/ forecasting and control is one of the greatest challenges facing the world today. The forecast of floods is achieved by the use of hydraulic models such as HEC-RAS, which are designed to simulate flow processes of the surface water. Extreme flood events in river Jhelum , lasting from a day to few are a major disaster in the State of Jammu and Kashmir, India. In the present study HEC-RAS model was applied to two different reaches of river Jhelum in order to estimate the flood levels corresponding to 25, 50 and 100 year return period flood events at important locations and to deduce flood vulnerability of important areas and structures. The flow rates for the two reaches were derived from flood-frequency analysis of 50 years of historic peak flow data. Manning's roughness coefficient n was selected using detailed analysis. Rating Curves were also generated to serve as base for determining the boundary conditions. Calibration and Validation procedures were applied in order to ensure the reliability of the model. Sensitivity analysis was also performed in order to ensure the accuracy of Manning's n in generating water surface profiles.

Keywords: flood plain, HEC-RAS, Jhelum, return period

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Authors: Fuchun Li, Hong Xiao

Abstract:

We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered.

Keywords: cut-off probability, early warning model, financial crisis, financial stress, regime-switching model, forecasting horizons

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Authors: Suk-Hyun Yu, Heeyong Kwon

Abstract:

Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.

Keywords: artificial intelligence, air quality prediction, neural networks, pattern recognition, PM-10

Procedia PDF Downloads 210

Authors: Agoston Restas

Abstract:

Introduction: This paper describes many initiatives and shows also practical examples which happened recently using Unmanned Aerial Systems (UAS) to support disaster management. Since the operation of manned aircraft at disasters is usually not only expensive but often impossible to use as well, in many cases managers fail to use the aerial activity. UAS can be an alternative moreover cost-effective solution for supporting disaster management. Methods: This article uses thematic division of UAS applications; it is based on two key elements, one of them is the time flow of managing disasters, other is its tactical requirements. Logically UAS can be used like pre-disaster activity, activity immediately after the occurrence of a disaster and the activity after the primary disaster elimination. Paper faces different disasters, like dangerous material releases, floods, earthquakes, forest fires and human-induced disasters. Research used function analysis, practical experiments, mathematical formulas, economic analysis and also expert estimation. Author gathered international examples and used own experiences in this field as well. Results and discussion: An earthquake is a rapid escalating disaster, where, many times, there is no other way for a rapid damage assessment than aerial reconnaissance. For special rescue teams, the UAS application can help much in a rapid location selection, where enough place remained to survive for victims. Floods are typical for a slow onset disaster. In contrast, managing floods is a very complex and difficult task. It requires continuous monitoring of dykes, flooded and threatened areas. UAS can help managers largely keeping an area under observation. Forest fires are disasters, where the tactical application of UAS is already well developed. It can be used for fire detection, intervention monitoring and also for post-fire monitoring. In case of nuclear accident or hazardous material leakage, UAS is also a very effective or can be the only one tool for supporting disaster management. Paper shows some efforts using UAS to avoid human-induced disasters in low-income countries as part of health cooperation.

Keywords: disaster management, floods, forest fires, Unmanned Aerial Systems

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Authors: Wiwat Kittinaraporn, Napat Harnpornchai, Sutja Boonyachut

Abstract:

This paper deals with the application of a novel neural network technique, so-called Support Vector Machine (SVM). The objective of this study is to explore the variable and parameter of forecasting factors in the construction industry to build up forecasting model for construction quantity in Thailand. The scope of the research is to study the non-residential construction quantity in Thailand. There are 44 sets of yearly data available, ranging from 1965 to 2009. The correlation between economic indicators and construction demand with the lag of one year was developed by Apichat Buakla. The selected variables are used to develop SVM models to forecast the non-residential construction quantity in Thailand. The parameters are selected by using ten-fold cross-validation method. The results are indicated in term of Mean Absolute Percentage Error (MAPE). The MAPE value for the non-residential construction quantity predicted by Epsilon-SVR in corporation with Radial Basis Function (RBF) of kernel function type is 5.90. Analysis of the experimental results show that the support vector machine modelling technique can be applied to forecast construction quantity time series which is useful for decision planning and management purpose.

Keywords: forecasting, non-residential, construction, support vector machines

Procedia PDF Downloads 414