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New Directions in Hazard and Disaster Science: Advances in Applied Sciences II

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 27010
Related Special Issue: New Directions in Hazard and Disaster Science: Advances in Applied Sciences

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Prof. Dr. Jason Levy

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Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
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Dear Colleagues,

Hazards, risk, and disasters—including geologic and hydrological processes, intentional threats, and health-related crises—are a growing menace to sustainability, economic development, and global security. For example, there are a wide variety of natural hazards (e.g., volcanic eruptions, earthquakes, landslides, mudflows, sinkholes, snow avalanches, flooding, and tsunamis) that pose a critical threat to pivotal infrastructure systems and life safety. Every year, terrorist attacks, severe natural events, and epidemics cause injuries and deaths on a large scale. Advances in hazard and disaster science and management are needed in order to cope with potentially hazardous human threats and geoprocesses.

This Special Issue examines a new set of applied science tools in the Big Data era that that can help to reduce the impact of these natural, technological, intentional, and health-related threats. There are advances in applied sciences that can directly reduce the likelihood, impact, and vulnerability of communities to disaster: Remote sensing; electrical, electronics, and communications engineering; nanotechnology and applied nanosciences; mechanical and civil engineering; applied biosciences and bioengineering; environmental and sustainable science and technology; applied physics; computing and artificial intelligence; Earth sciences and geography; applied industrial technologies. For example, new approaches in data science and machine learning capitalize on the ubiquity of risk and hazard data sets, as well as on advances in remote sensing, global position systems, and GIS. These solutions also provide new opportunities for the analysis and management of all types of disaster risks.

Prof. Dr. Jason K. Levy
Guest Editor

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Keywords

Applied industrial technologies for managing natural hazards
Environmental and sustainable science and technology and disaster prevention
Technological risks and critical infrastructure protection
Systems engineering for disaster risk reduction
Geohazards analysis with Earth sciences and geography

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Published Papers (8 papers)

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Research

17 pages, 4671 KiB

Open AccessArticle

A Percentile Method to Determine Cold Days and Spells in Bangladesh

by Md. Mahbub Alam, A. S. M. Mahtab, M. Razu Ahmed and Quazi K. Hassan

Appl. Sci. 2023, 13(12), 7030; https://doi.org/10.3390/app13127030 - 11 Jun 2023

Viewed by 1528

Abstract

The 10th percentiles (10P) of the daily minimum (T_min) and maximum (T_max) during 1971–2000 were determined to estimate a threshold for cold days. This 10P (a standard of extreme climatic condition suggested by the World Meteorological Organization) threshold was applied with the daily T_min and T_max in the winter months (December, January, and February) of 2000 to 2021 to calculate the number of cold days, and consecutively, cold spells, and their trends. A cold day was declared when the daily T_max and/or T_min was lower than that of the 10P threshold, and the average temperature was ≤17 °C in a weather station. In this research, the cold days and spells were categorized into five classes, namely extreme (≤13 °C), severe (>13–14 °C), very (>14–15 °C), moderate (>15–16 °C), and Mild (>16–17 °C). Moreover, a cold spell was considered when such cold days persisted for ≥2 consecutive days in at least two nearby stations. The results revealed a higher number of average cold days during winter in the western and northwestern districts of Bangladesh, and it reduced gradually in the south, southeast, and northeast. Dinajpur and Rajshahi districts showed the highest number of extreme and severe categories of cold days, i.e., 4.81 and 3.24 days/year, respectively. Rajshahi division had the highest number of cold spells on average (3.24/year), and Rangpur division had the highest number of extreme-category (the category that carries the lowest temperature range, ≤13 °C) cold spells (1.29/year). January was the coldest month, with the maximum number of cold days and spells. The highest average number of cold days (25.54%) was observed during the second ten days of January (i.e., 11–20 January). Significant increasing trends were found in the cold days of 11–20 December (5 stations), 21–31 December (3 stations), and the month of December (13 stations). In contrast, significant decreasing trends were noticed for the 1–10 January period in three weather stations. Our proposed 10P method could be used to determine the cold days and spells in Bangladesh that might be useful for the policy makers in formulating appropriate strategies in minimizing the impact of cold regimes during the winter season. Full article

(This article belongs to the Special Issue New Directions in Hazard and Disaster Science: Advances in Applied Sciences II)

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15 pages, 14512 KiB

Open AccessArticle

Implementation of Petrographical and Aeromagnetic Data to Determine Depth and Structural Trend of Homrit Waggat Area, Central Eastern Desert, Egypt

by El Saeed R. Lasheen, Waheed H. Mohamed, Antoaneta Ene, Hamdy A. Awad and Mokhles K. Azer

Appl. Sci. 2022, 12(17), 8782; https://doi.org/10.3390/app12178782 - 31 Aug 2022

Cited by 18 | Viewed by 1792

Abstract

In the current study, we conducted petrographic investigation combined with aeromagnetic data in order to classify variable granitic rocks, delineate structural trends and deduce depth of the basement rocks cropping out in Homrit Waggat area, Central Eastern Desert, Egypt. Field and petrographic investigations revealed that the granitic Homrit Waggat rocks include two groups. The first group includes the older granitic rocks, comprising tonalites and granodiorites. In contrast, the second one includes younger granitic rocks, involving alkali-feldspar granites, syenogranites and albitized granites. Depth as well as subsurface structures can be identified using magnetic method. Two tectonic maps representing the deep-seated and the shallow-seated structural features were constructed to show the structural history of the study area. The major tectonic trends indicate that the regional structures are controlled by deeper structures which have NW–SE, NNE–SSW—NE–SW and N–S directions. On the other hand, we find that the local structure trends are controlled by the local shallow structures that have NNE–SSW, NNW–SSE, ESE–WNW and N–S directions. Depth levels of the economic rare metal-bearing rocks range from 0 km to 1.2 km (Euler deconvolution technique) and from 0 km to 2.3 km (the analytical signal profiles) by using the aeromagnetic data, reflecting large resources of rare metal-bearing rocks. Full article

(This article belongs to the Special Issue New Directions in Hazard and Disaster Science: Advances in Applied Sciences II)

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20 pages, 3369 KiB

Open AccessArticle

The Spatial Optimization of Emergency Shelters Based on an Urban-Scale Evacuation Simulation

by Wei Chen, Yijun Shi, Wei Wang, Wenjing Li and Chao Wu

Appl. Sci. 2021, 11(24), 11909; https://doi.org/10.3390/app112411909 - 14 Dec 2021

Cited by 5 | Viewed by 3309

Abstract

As an important space for disaster prevention, the construction of emergency shelters is crucial for the creation of a complete disaster relief facility network. Based on the goal of the prevention of day and night disaster, short-term fixed shelters are taken as the study object of the present work, and models are designed for evacuation simulation and the spatial optimization of shelters. According to the simulation, 680 of the 2334 demand points were found to be incompletely evacuated, and the average time for everyone to be evacuated was 10.3 min. Moreover, of the 888 short-term fixed shelters, only 218 did not reach their maximum capacity. In the context of short-term fixed sheltering, Haizhu was found to have the largest number of non-evacuated people (1.11 million), and the average number of non-evacuated people in Yuexiu was the largest (2184). According to the spatial optimization data of the shelters, the numbers of target plots for new shelter resources that must be added in Haizhu, Yuexiu, Liwa, and Tianhe are 406, 164, 141, and 136, respectively, the effective shelter areas of which are 2,621,100, 2,175,300, 812,100, and 1,344,600 m², respectively. A total of 487 short-term fixed shelters and 360 temporary shelters were newly added, and the recommended scales for Haizhu, Liwan, Tianhe, and Yuexiu were 243, 70, 58, and 116, respectively, with average effective areas of 6169 m², 5577 m², 8707 m², and 12,931 m², respectively. Additionally, the recommended scales of newly added temporary shelters in Haizhu, Liwan, Tianhe, and Yuexiu are 163, 71, 78, and 48, with an average effective area of 2706, 2581, 4017, and 6234 m², respectively. These findings provide a direct quantitative basis for the spatial optimization of various types of emergency shelters, and the method proposed in this paper supports the planning and layout of emergency shelters, as well as the improvement of the efficiency of urban resource allocation. Full article

(This article belongs to the Special Issue New Directions in Hazard and Disaster Science: Advances in Applied Sciences II)

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18 pages, 8042 KiB

Open AccessArticle

Examining Post-Fire Perceptions of Selected Mitigation Strategies after the 2016 Horse River Wildland Fire in Alberta, Canada

by Quazi K. Hassan, Khan Rubayet Rahaman, M. Razu Ahmed and Sheikh M. Hossain

Appl. Sci. 2021, 11(21), 10155; https://doi.org/10.3390/app112110155 - 29 Oct 2021

Cited by 8 | Viewed by 2453

Abstract

Our aim was to study post-fire perceptions of selected mitigation strategies for wildland fire-induced risks proposed in a previous scientific study for the communities situated within the forested areas. Consequently, we considered engaging relevant professionals in the Regional Municipality of Wood Buffalo (RMWB), Alberta who experienced the costliest wildland fire occurrences in Canadian history known as the 2016 Horse River Fire (HRF). To meet our goal, we formulated a questionnaire based on the scientific evidence presented in a previous study and conducted a structured survey. Our results revealed that 24 professionals participated in the survey during the June 2020–April 2021 period, providing a 32% response rate. We observed that a high percentage of the participants agreed (i.e., between 63% and 80%) with the proposed wildland fire-induced risk mitigation strategies, including the presence of no to little vegetation in the 30 m buffer zone from the wildland–urban interface (WUI), extending the 30 m buffer zone to 70 m from the WUI, constructing a 70 m width ring road around the communities, and parking lots of the social infrastructures in the fringe of the communities encountering to the forest. We also found other views, including the use of non-combustible and fire-resistant construction materials, and developing the 70 m buffer zone as a recreational space. Full article

(This article belongs to the Special Issue New Directions in Hazard and Disaster Science: Advances in Applied Sciences II)

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22 pages, 3821 KiB

Open AccessArticle

Assessment of the Vulnerability of Selected Key Elements of Rail Transport. Slovak Case Study

by Eva Sventekova, Zdenka Urbancova and Katarina Holla

Appl. Sci. 2021, 11(13), 6174; https://doi.org/10.3390/app11136174 - 2 Jul 2021

Cited by 9 | Viewed by 2302

Abstract

Increases in means of transport, developed transport networks, and modernization dependent on computer technology not only lead to huge security demands but also require preventive measures and vulnerability assessments of key elements of transport infrastructure. The analyses carried out have shown that, though the importance of transport continues to grow, there is currently no system for assessing the vulnerability of essential elements that are necessary to keep rail transport safe and operational. Taking into account a number of criteria, the team of authors identified key elements of the railway transport infrastructure—bridges, tunnels, the width of the track, and the marshaling yard. The criteria applied included the significance and uniqueness of the element, its technical parameters, the difficulty of repair after possible damage, potential risks related to the location, and the analysis of rail safety based on statistical data and safety reports. The aim of this contribution is to present a multi-level model for assessing the vulnerability of key elements of rail transport infrastructure. The authors proceeded from the hypothesis that the vulnerability of key elements can be assessed using quantitative and qualitative parameters of the individual elements. The added value of the model is the methodological basis for a comprehensive vulnerability assessment system that will allow competent authorities to objectify the process of vulnerability assessment of key elements and to set up appropriate safeguards to enhance rail safety. The proposed model was verified on the Zilina-Vrútky track section, which is considered one of the most important railway junctions in Slovakia. Full article

(This article belongs to the Special Issue New Directions in Hazard and Disaster Science: Advances in Applied Sciences II)

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20 pages, 1633 KiB

Open AccessArticle

DisBot: A Portuguese Disaster Support Dynamic Knowledge Chatbot

by João Boné, João C. Ferreira, Ricardo Ribeiro and Gonçalo Cadete

Appl. Sci. 2020, 10(24), 9082; https://doi.org/10.3390/app10249082 - 18 Dec 2020

Cited by 15 | Viewed by 5392

Abstract

This paper presents DisBot, the first Portuguese speaking chatbot that uses social media retrieved knowledge to support citizens and first-responders in disaster scenarios, in order to improve community resilience and decision-making. It was developed and tested using Design Science Research Methodology (DSRM), being progressively matured with field specialists through several design and development iterations. DisBot uses a state-of-the-art Dual Intent Entity Transformer (DIET) architecture to classify user intents, and makes use of several dialogue policies for managing user conversations, as well as storing relevant information to be used in further dialogue turns. To generate responses, it uses real-world safety knowledge, and infers a dynamic knowledge graph that is dynamically updated in real-time by a disaster-related knowledge extraction tool, presented in previous works. Through its development iterations, DisBot has been validated by field specialists, who have considered it to be a valuable asset in disaster management. Full article

(This article belongs to the Special Issue New Directions in Hazard and Disaster Science: Advances in Applied Sciences II)

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32 pages, 21919 KiB

Open AccessArticle

Influence of Rainfall Intensity on the Stability of Unsaturated Soil Slope: Case Study of R523 Road in Thulamela Municipality, Limpopo Province, South Africa

by Fhatuwani Sengani and François Mulenga

Appl. Sci. 2020, 10(24), 8824; https://doi.org/10.3390/app10248824 - 10 Dec 2020

Cited by 14 | Viewed by 4595

Abstract

The purpose of this paper was to analyze the impact of extreme rainfall on the recurrence of slope instability using the Thulamela Municipality roads (R523) as a case study. To this end, the historical rainfall data of the area of study were analyzed between 1988 and 2018. The results show that a significant increase in rainfall is usually experienced in the summer months of December and January. Following this, the factor of safety (FoS) of slopes of silt clay, clay, and clay loam soils were estimated using the SLIDE simulator (Numerical software “Finite Element Method (FEM)”) under sunny to rainy conditions of the area. A complementary model, FLACSlope (Numerical software “Finite Difference Method (FDM)”), was utilized to simulate FoS and pore water pressure in sunny and rainy conditions of the area. Simulation results show that extreme rainfall has the ability to reduce the shear strength and resistance of the soil slope material. This may explain the recurrent landslides noted in the area. Finally, the water pore pressure has been simulated to increase with the increased water table, which generally pushes the soil particles apart and reduces the stress between the particles resulting in soil slope failure. Extreme rainfall alters the phase of the material solid in a manner that may require further research for a better understanding. Full article

(This article belongs to the Special Issue New Directions in Hazard and Disaster Science: Advances in Applied Sciences II)

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18 pages, 5064 KiB

Open AccessArticle

Data-Driven Approach for Incident Management in a Smart City

by Luís B. Elvas, Carolina F. Marreiros, João M. Dinis, Maria C. Pereira, Ana L. Martins and João C. Ferreira

Appl. Sci. 2020, 10(22), 8281; https://doi.org/10.3390/app10228281 - 22 Nov 2020

Cited by 8 | Viewed by 4368

Abstract

Buildings in Lisbon are often the victim of several types of events (such as accidents, fires, collapses, etc.). This study aims to apply a data-driven approach towards knowledge extraction from past incident data, nowadays available in the context of a Smart City. We apply a Cross Industry Standard Process for Data Mining (CRISP-DM) approach to perform incident management of the city of Lisbon. From this data-driven process, a descriptive and predictive analysis of an events dataset provided by the Lisbon Municipality was possible, together with other data obtained from the public domain, such as the temperature and humidity on the day of the events. The dataset provided contains events from 2011 to 2018 for the municipality of Lisbon. This data mining approach over past data identified patterns that provide useful knowledge for city incident managers. Additionally, the forecasts can be used for better city planning, and data correlations of variables can provide information about the most important variables towards those incidents. This approach is fundamental in the context of smart cities, where sensors and data can be used to improve citizens’ quality of life. Smart Cities allow the collecting of data from different systems, and for the case of disruptive events, these data allow us to understand them and their cascading effects better. Full article

(This article belongs to the Special Issue New Directions in Hazard and Disaster Science: Advances in Applied Sciences II)

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