INFORMATION SYSTEM FRAMEWORK FOR INTEGRATED RISK ASSESSMENT FROM NATURAL DISASTERS

Creation of a consistent 3D model of a city requires accurate data. Usually, accuracy assurance problems of data are solved by time consuming and expensive process of collecting and aligning with ground control points (GCP). Therefore, alternative methods become important. Using existing Geographic Information Systems (GIS) databases may decrease the time and cost of creating a reference dataset by reducing the number of GCPs required for producing high quality 3D data or GIS databases can serve as reference data. For this purpose, new spatial data analysis methods are needed to assure that GIS databases are of high-quality. In this paper, we propose a novel methodology and its sample development for comparative spatial analysis of digitized point cloud and the corresponding GIS database in order to statistically assess opportunities to align Mobile Mapping Systems (MMS) data with existing GIS databases or to improve involved datasets. The method is evaluated using LiDAR data provided by Estonian company Reach-U Ltd. and GIS database layers from different Estonian open and closed databases.

Emergencies happen every day. Many are caused by storms or auto accidents and can be planned for, if not predicted. Emergencies resulting from natural hazards often affect a large number of people, and planning for them can be difficult, since knowledge of the needs of the people involved is generally unavailable. Emergencies resulting from accidents at industrial and military facilities can also be large scale in nature if people must be evacuated or sheltered in place. Federal planning for large scale emergencies is the responsibility of the Federal Emergency Management Agency (FEMA), which provides assistance to various emergency management agencies at the national, state and local level. More information about FEMA is available at http://www.fema.gov/. The purpose of the Special Population Planner (SPP) is to help emergency planners address the needs of persons with special needs. The exact definition of 'special population' is a policy decision. Policymakers have included a variety of groups in this term, such as persons with disabilities, those who do not have vehicles with which to evacuate, children who are unattended at times (latchkey children), and many others. The SPP was developed initially for the Alabama Emergency Management Agency as part of its Chemical Stockpile Emergency Preparedness Program (CSEPP), which aids emergency planning and preparedness in communities surrounding military installations across the United States where chemical weapons are stored pending their destruction under federal law. Like that specialized application, this open-source version contains a set of specialized Geographic Information System (GIS) tools to facilitate emergency planning on behalf of persons with special needs, regardless of how the term is defined. While the original SPP system was developed for emergency planning relating to chemical hazards, it can be applied to other threats as well. It is apparent from Hurricane Katrina and other natural and man-made disasters that many of the problems posed by emergency planning for a chemical weapons agent release are shared by other hazards as well. The notion that emergency planning shares common functions underlies the decision by FEMA to include the Chemical Stockpile Emergency Preparedness Program (CSEPP) in its 'all-hazards' planning approach. The CSEPP's official planning guidance operationalizes this approach by suggesting that state and local CSEPP emergency plans 'should be appended to the existing all-hazards emergency plan.' The SPP is programmed as a set of tools within an ESRI ArcMap 9.1 project. ArcMap is a component of both ESRI ArcGIS 9.1 and ESRI ArcView 9.1, and it provides a rich GIS user interface for viewing spatial and tabular data, analyzing it, and producing output reports and maps. This GIS interface has been augmented with the SPP tools for a user interface that provides custom functionality for emergency planning. The system as released also includes some hypothetical example records for special needs populations, facilities, resources, control points and sirens sufficient for showing how the system would work with real information. A GIS database is included with some publicly available example layers. The SPP is designed to support emergency planners as they address emergency management issues, and includes capabilities that support the collection and importing of data, the review of data in a spatial context, and GIS tools for emergency planning. The SPP system allows for the identification and categorization of response zones to allow for multiple levels of preparedness. An Immediate Response Zone (IRZ) might be designated as the area 0 to 10 miles from a facility where the response would be the most urgent. SPP can support more than one set of planning zones to accommodate different types of emergencies or the different jurisdictions of emergency response organizations. These areas can be delineated by any number of criteria that make sense for the area. An area like New Orleans might designate response zones based on the depth above/below sea level, or an urban area may categorize an IRZ as the 'central business district' with the PAZ encompassing the larger outlying area. Many of the sites involved in the CSEPP currently use an integrated emergency response information system to support their emergency management planning. The D2-Puff system (including or WebPuff 2.1) by Innovative Emergency Management, Inc. is the main software serving this purpose. More information on D2-Puff can be found at www.ieminc.com. SPP differs from D2-Puff in that it is designed for planning, not response to assist those with special needs, is programmed within a popular commercial GIS software system, and can be loaded with large amounts of data to support all hazards emergency planning.

Natural disasters and their impacts on the silica losses from agriculture in China from 1988 to 2016

Indonesia is a country located in a strategic geological position and is at the confluence of three world plates. This position causes Indonesia to become a country frequently hit by natural disasters such as earthquakes, landslides and other natural disasters. Based on data obtained from the Regional Disaster Management Agency (BPBD), from year to year the number of natural disasters always increases. The increase in the occurrence of natural disasters has led to the need for further research to find out how vulnerable areas are in Indonesia and can later reduce the risks posed when natural disasters occur. Researchers will use the K-Means Clustering method to classify natural disaster areas based on their level of vulnerability to natural disasters. Clustering is a process for grouping data into several clusters or groups. K-Means is a method for grouping non-hierarchical data (partitions) which can divide data into two or more groups. The source of this dataset comes from the Central Bureau of Statistics (BPS) and consists of 12 columns and the data used are landslides and no natural disasters. Then a search for the optimal K value was carried out using the elbow method and Silhouette Analysis which produced 2 clusters namely cluster 0 and cluster 1. The results of the clustering indicated that cluster 1 was classified as a disaster-prone area in Indonesia including Aceh, North Sumatra, West Java, Central Java, East Java.

The Changes for Ischaemic Heart Disease Mortality at the Global Level and Their Association with Natural Disaster: A 28-Year Ecological Trend Study in 193 Countries

The primary aim of this research is to determine the awareness of science teacher candidates regarding natural disasters. This research aims to reveal the level of awareness towards natural disasters before and after the education received by science teacher candidates. The participant group of the study consists of 19 science teacher candidates studying at the faculty of education of a public university. Based on the findings, it can be concluded that science teacher candidates have a high perception and awareness of natural disasters. Despite being final-year students, the participants have not attended additional training on this subject, even though natural disasters are quite frequent in our country, indicating a lack of sufficient studies in this area. In this context, it is believed that organizing preparatory and awareness-raising trainings related to natural disasters in faculties of education could provide significant contributions in terms of emotional control and disaster prevention, and that the knowledge and skills of science teacher candidates could be utilized in potential natural disasters.

This study aims to create an integrated geographical information system (GIS) database of natural resources represented by mining activities in the Red Sea area in Sudan. GIS is a vital tool to help the decision‐makers in managing and classifying these resources in terms of quantity and quality within the concept of sustainable development. The paper extracts some models of investment map indicators. In addition to that, it conducts a study and research aimed at developing a mineral resources management and discovering and identifying the new areas of mineral wealth in GIS database. The motivation of this study stems from the fact that countries with these types of wealth can greatly grow their gross domestic product (GDP) through the optimum management and governance. Most of the least development countries (LDC) can focus on the search for alternative natural resources (beside other conventional resources, i.e., agriculture) to support their economy. Red Sea region is one of the great regions full of natural and mineral resources (specifically gold and minerals), beside its strategic location on the way of most of the global trade transaction path. This paper designs and implements GIS multiple database layers. The multiple layers were modelled to overcome the technical difficulties that result from processing of large quantities of tempospatial information. The database was built by using an Oracle database system due to its capability of multilayer design.

China is a country where natural disasters happen frequently, and the losses caused by natural disasters have offset the great wealth accumulated by the construction of China's economy, and has become an impediment to the sustained and stable development of China's economy to a certain extent. Through analyzing, the article concludes that the occurrence of natural disasters is not conducive to the growth of China's economy according to production function, and in the last ten years, the annual growth rate of the economy has been affected by 0.29 percent on average because of the occurrence of natural disasters according to the empirical analysis based on Harrod-Domar model. The article suggests to reduce the impact of natural disasters on economic growth by increasing the investment in fixed assets and decreasing the replacement investment in disaster losses.

A deep learning algorithm-driven approach to predicting repair costs associated with natural disaster indicators: The case of accommodation facilities

Purpose: The objective of this work is to identify the relationship between the natural disasters that occurred in the municipality of Novo Progresso-PA in the period from 2021 to 2023 with extreme rain events and through it highlight factors that contributed to them. Method/design/approach: Volume data (m³) were obtained from the website National Center for Monitoring and Early Warning of Natural Disasters (CEMADEM). For rainfall volume data tabulation, computational software and programs such as Excel (OFFICE) were used to create spreadsheets and charts to identify rainfall patterns and volumes. Results and conclusion: The results showed that during the annual rainy season, there was a total rainfall exceeding 200 mm/month, with an annual volume of 2,006.33 mm/year. The highest monthly volume was recorded in March, with an average of 396.33 mm/month. It was found that during the analyzed period, there were three (3) natural disasters—one in each year—of a hydrological nature, caused by local/convective storms with intense rainfall, where the peripheral and rural areas of the municipality were the most affected due to the lack of adequate infrastructure, such as an urban drainage system capable of handling seasonal demands and the region's climatic variations. Research implications: The intensification of extreme rainfall, driven by climate change, results in natural disasters, causing significant human and material losses. An increase in recorded natural disasters has been observed in the municipality of Novo Progresso, which demands urgent structural interventions and public policies to mitigate the impacts and protect vulnerable populations in both urban and rural areas. Originality/value: Originality/value: The originality of this theme underscores the urgency of mitigation and adaptation strategies that take local vulnerabilities into account, which are essential for protecting communities and formulating effective public policies.

The environmental impact of a hazardous material spill is a complex function of the material's physical and chemical characteristics and the local environmental conditions in which it is spilled. This study develops a geographical probability distribution for two important environmental parameters affecting this impact: soil type and groundwater depth. The paper assesses the probability of exposure of various soil types and groundwater depth regions to hazardous material spills from railroad tank car accidents. The State Soil Geographic database (STATSGO), a geographic information system (GIS) database from the U.S. Department of Agriculture, was used to analyze soils, and real-time groundwater data from the U.S. Geological Survey were used to analyze groundwater. The “rail2m” GIS database available from the Bureau of Transportation Statistics was used to conduct overlay analyses with the soil GIS data to obtain the probability of occurrence of various soil types under rail lines. The same method could not be used for groundwater exposure because no suitable GIS database for this parameter was available. Therefore, a nationwide probability distribution of the groundwater depth ranges was developed. The proportion of the three soil types under rail lines was 44.0% for sand, 51.2% for silt, and 4.0% for clay, the remainder being over surface water. The probability of occurrence of five groundwater depth regions was 16.4% for 5 to 15 ft, 12.6% for 15 to 25 ft, 23.3% for 25 to 75 ft, 12.0% for 75 to 125 ft, and 35.9% for greater than 125 ft. A matrix of the probability distribution of environmental scenarios was developed; it is suitable for use in conducting environmental risk assessments of railroad transportation of hazardous materials.

The article discusses the issue of predicting the consequences of natural disasters in forests using geographic information systems (GIS) and related tools. The authors emphasize the importance of improving the quality of management and monitoring of forest resources, as well as the need to improve the perception of geoinformation in a form accessible to the user. Analysis of information systems shows that they can be used to monitor forest plantations, promptly assess the consequences of natural disasters and subsequently make informed decisions on their elimination. The authors also propose an algorithm for solving the problem of assessing and predicting the consequences of natural disasters in forests, describe the necessary conditions and initial data on the characteristics of the objects under study and the parameters being determined. In addition, using data from information systems that can be used to predict natural disasters in forest areas, recommendations are offered and the feasibility of using GIS is established, justifying the corresponding equipment and its characteristics. Overall, the article explores different approaches to forecasting and recommends using an integrated approach using modern information technologies in the field of analytics and machine learning to achieve the best results.

Natural disaster is an occurrence that threatens and disrupts the lives of living things caused by natural factors and causes environmental damage, casualties, and so on. One of the regions in Indonesia that experienced natural disasters with high intensity in the 2020-2022 timeframe is West Java Province. The purpose of this study is to group regency/city in West Java Province based on the occurrence of natural disasters in 2020-2022 and the characteristics of each cluster formed. This research uses spatio temporal data applied to the Spatio Temporal-Density Based Clustering of Application with Noise (ST-DBSCAN) algorithm using 3 variables, namely the incidence of floods, landslides, and tornadoes in West Java Province in 2020-2022. This study uses the parameters epsilon 1 of 10, epsilon 2 of 30, and minimum points of 3 using the K-NN algorithm to form clusters from the ST-DBSCAN algorithm. The results showed that 2 clusters and 11 noise points were formed with a silhouette coefficient value of 0.5176, where cluster 1 consisted of 66 regency/city, cluster 2 consisted of 4 regency/city and 11 regency/city were included in the noise.

There has been a steady increase in the occurrence of natural disasters. Yet their effect on economic growth remains unclear, with some studies reporting negative, and others indicating no, or even positive effects. These seemingly contradictory findings can be reconciled by exploring the effects of natural disasters on growth separately by disaster and economic sector. This is consistent with the insights from traditional models of economic growth, where production depends on total factor productivity, the provision of intermediate outputs, and the capital-labor ratio, as well as the existence of important intersector linkages. Applying a dynamic Generalized Method of Moments panel estimator to a 1961-2005 cross-country panel, three major insights emerge. First, disasters affect economic growth - but not always negatively, and differently across disasters and economic sectors. Second, although moderate disasters can have a positive growth effect in some sectors, severe disasters do not. Third, growth in developing countries is more sensitive to natural disasters - more sectors are affected and the magnitudes are non-trivial.

In this research, a passenger-centric passenger information system is proposed, which uses crowd sourcing and mobile phones so that passengers are not only information consumers but are also providers of information to the system. Passengers can allow the system to track their location through their smart phones when they are traveling on public transportation; this will compensate for the lack of a vehicle tracking system in public transportation, particularly in nonurban areas. Map-matching (MM) algorithms integrate location data (i.e.,latitude and longitude) obtained from positioning sensors (in this case, mobile GPS) with a digital geographic information system (GIS) road map. Any map-matching algorithm first identifies the road link on which a vehicle is traveling and then determines the vehicle's location on that road segment. In the proposed information system, at a given point of time, a number of vehicle locations (latitude and longitude) are received from passengers traveling on a bus. In order to provide a precise vehicle location at a given point of time, a novel map-matching algorithm using fuzzy logic, which integrates multiple vehicle locations (obtained from passenger's smart phones) with a GIS road map, has been developed. The developed map-matching algorithm was tested using real-world data collected on four different bus routes in Aberdeenshire, Scotland, and also GPS data collected in and around Nottingham, United Kingdom. It was identified that the developed MM algorithm is efficient and capable of supporting the proposed passenger information system. (C) 2013 American Society of Civil Engineers.