Disaster Intensity Research Articles

Suppression characteristics of water mist containing alkali metal compounds in natural gas explosions

The natural gas explosion in semi-constrained spaces seriously jeopardize public safety. Water mist containing additive is an important method to reduce the disaster intensity. However, in actual scenarios, the explosion usually propagates for a certain distance before entering the mist area, which is more difficult to suppress the explosion. Therefore, the self-designed experimental system was used to recognize the suppression effect of water mist containing alkali metal compounds under the simulated scenario. The results show that the flame propagation distance was significantly reduced, the shortest overfire range was decreased by 40.0 % compared with the water mist condition. The peak overpressure and pressure rise rate also showed a significant decrease, the maximum pressure drop rate reached 72.3 %. When the explosion flame entered the atomized region, the turbulent disturbance generated by the atomization accelerated the mixture of combustion zone and the unburned area, which increases the possibility of consuming free radicals. Compared with the KCl and NaCl, the mist containing K2CO3 and Na2CO3 share higher suppression capacity. This study provides scientific guidance for the practical application of water mist explosion suppression.

Quantitative characterization of granite failure intensity under dynamic disturbance from energy standpoint

Abstract Quantitative evaluation of rock dynamic disaster intensity is a challenging and difficult task in the field of earth science. In this article, the dynamic compression tests of granite under different impact velocities are carried out, and the energy evolution characteristics of the dynamic failure process of granite are analyzed. The dynamic fracture characteristics of granite were studied by using computed tomography and section scanning equipment. The results show that the energy dissipation and energy accumulation mechanism of granite under dynamic loading are fundamentally changed with the increase of impact disturbance strength, which leads to the transformation of the failure mode from the tension-type sheet crack under low-speed impact to the complex network crack coupled by tension and shear under high-speed impact. At the same time, the greater the impact velocity, the larger the macroscopic crack density, the more uneven the particle distribution on the fracture section, the greater the roughness, and the higher the rock mass fracture degree. In accordance with the results, the energy distribution ratio is proposed to quantitatively characterize the coupling effect of energy dissipation and energy accumulation in the dynamic failure process of granite and to reflect its dynamic failure intensity. The higher the energy distribution ratio, the stronger the energy coupling effect and the more intense the dynamic failure of the rock mass.

Optimal Resilience and Risk-Driven Strategies for Pre-Disaster Protection of Electric Power Systems against Uncertain Disaster Scenarios

Pre-disaster protection strategies are essential for enhancing the resilience of electric power systems against natural disasters. Considering the budgets for protection strategies, the dependency of other infrastructure systems on electricity, and the uncertainty of disaster scenarios, this paper develops risk-neutral and risk management models of strategies for pre-disaster protection. The risk-neutral model is a stochastic model designed to maximize the expected value of resilience (EVR) of the integrated system. The risk management model is a multi-objective model prioritizing the minimization of risk metrics as a secondary goal alongside maximizing the EVR. A case study conducted on the energy infrastructure systems in the Greater Toronto Area (GTA) validates the effectiveness of the models. The findings reveal the following: (i) increasing the budget enhances the EVR of the integrated system; however, beyond a certain budget threshold, the incremental benefits to the EVR significantly diminish; (ii) reducing the value of the downside risk often results in an increase in the EVR, with the variation in Pareto-optimal solutions between the two objectives being non-linear; and (iii) whether for the risk-neutral or risk management protection strategies, there are reasonable budgets when considering disaster intensity and the cost of protection measures. The models can help decision-makers to select effective protection measures for natural disasters.

Comprehensive Risk Assessment of Farmer Households in Flood and Earthquake Prone Areas Based on Multidimensional Information Diffusion Model

In recent years, the global practice of regional natural disaster reduction and comprehensive risk prevention has promoted the integration of multiple disciplines, and the theory and technology of comprehensive risk assessment for multiple natural disaster chains urgently need innovation. This article is based on multiple natural disaster chain scenarios and sets secondary indicators for multiple influencing factors based on the first level indicators composed of basic risk factors such as hazard factors, vulnerability of disaster bearing bodies, and disaster prevention and reduction capabilities. By using multidimensional information diffusion technology, multiple hazard factor hazard combination factors, vulnerability combination factors of disaster bearing bodies, and comprehensive disaster prevention and reduction capability combination factors reflecting various natural disaster scenarios are constructed, A household comprehensive risk assessment index system for multi-dimensional information diffusion assessment of natural disaster comprehensive risk was designed by quantifying risk factors such as the intensity of comprehensive disaster causing factors, vulnerability of disaster bearing bodies, and comprehensive disaster prevention and reduction capabilities in disaster scenarios. This paper expands the traditional information diffusion comprehensive risk assessment model, makes up for the lack of information on geographical units, establishes a multi impact factor multi dimension natural disaster risk comprehensive assessment model for multi risk factors of complex disaster scenarios with small samples, obtains the theoretical value of multi-dimensional information Diffusion model comprehensive risk assessment for complex disaster scenarios, breaks through the sample size limit of natural disaster risks, and makes up for the lack of information on small samples of small probability events, Improved the reliability and accuracy of comprehensive risk assessment for multiple natural disaster chains. Then, take the disaster scenario formed by the comprehensive risk of earthquake and flood in a place in the upper reaches of the Fujiang River basin as the analysis sample, calculate the comprehensive risk assessment value of flood and earthquake damage in the study area's micro samples (rural households in villages and towns). By comparison, it is found that the comprehensive risk value assessed by the multi-dimensional information Diffusion model has good reliability and high accuracy, which is significantly better than the traditional Kriging interpolation method and geographical weighted regression method. This article provides a comprehensive risk assessment of natural disasters at the micro household level, which can better reflect the diversity and regionality of risks, help clarify the hidden risks of rural natural disasters, and provide scientific basis for government emergency management decision-making.

The impact of floods and cyclones on fiscal arrangements in India: An empirical investigation at the sub-national level

Disasters can inflict severe devastation on communities and territories at all levels of authority. With regard to the connection between floods and cyclones and their financial impact, this study seeks to refocus national and subnational research efforts. The consequences of floods and cyclones on India's budget and economy at the subnational government level are examined in this study. The study creates a physical disaster intensity index to evaluate the impact on fiscal sustainability after disasters, as the estimated damages statistics are unreliable. Between 1995 and 2018, the study integrated disaster intensity index and budgetary data to produce a panel dataset of 25 Indian states. Using Panel Vector Autoregression, the study discovers that a state's overall government expenditure goes up, and its budget deficit worsens when floods and cyclones strike. By further breaking up the fiscal variables, we could pinpoint specific areas where disasters have exerted their influence, especially in terms of decreased own tax revenue, increased social sector expenditure and capital outlay, increased outstanding liability, increased primary deficit, and reduced transfers from the centre over time. It observed that after disasters, states more prone to disasters have higher volatility in revenue generation and expenditure patterns, and a sharp increase in outstanding liabilities. This suggests a greater reliance on borrowing for disaster recovery. Less-prone states, however, show higher utilization rates of disaster relief funds, indicating better resource allocation for disaster response and recovery. The study highlights that state governments rely highly on ex-post Disaster Risk Financing instruments, especially debt financing, as national relief and response funds couldn't manage the increased spending and budget deficit from disasters. This study thus helps policymakers and governments look for more proactive disaster financing instruments and make changes in India's fiscal framework to integrate climate change considerations.

Implementation of Text Processing Techniques on Citizen Opinions Regarding Floods in Surabaya

An analysis of residents opinions on flooding in Surabaya is essential to identify their perceptions and aspirations towards this disaster. This can facilitate the making of appropriate flood mitigation policies. The current problem is that it is difficult to retrieve data from X in the form of both users and tweets automatically. So that in some studies that use tweet data becomes less efficient in the data collection process. This research confirmed the use of messages to determine highly impactful disaster zones and showed how tweets can be used to identify oscillations in disaster intensity over time. The topic of this research is to apply the Data Crawling method to obtain datasets on social media X. Then the next method is text preprocessing using Wordcloud, Matplotlib, (NTLK) Natural Language Toolkit, and Sastrawi libraries. In natural language processing, the data to be extracted includes unstructured or “arbitrary” data. In normal dialect preparing (NLP), the information to be extracted includes unstructured or "self-assertive" information. For future purposes (assumption examination, subject modeling, etc.), such information must be changed over into organized data. The discoveries of the think about can help the organization in comprehending the necessities and inclinations of the people with respect to surges. This article demonstrates how Artificial Intelegence may be applied to text data analysis in order to provide insightful findings. the outcomes of this research can help the government in making more effective policies to overcome flooding in Surabaya

Provincial-scale assessment of vulnerability and resilience to drought in China

Frequent droughts have caused severe disaster losses in China. Such events can be minimized by enhancing the country's resilience and reducing its vulnerability, where this can ensure socioeconomic stability and sustainable development. Evaluating the vulnerability and resilience to drought is thus crucial for effectively managing the risk of disasters and promoting sustainable socioeconomic development. In this study, we constructed a comprehensive framework to assess the spatiotemporal characteristics of China's vulnerability and resilience to drought at the provincial scale from an input-output perspective by using the Super-efficiency Data Envelopment Analysis (DEA) model and the Super-efficiency Slacks-Based Measure DEA (SBM-DEA) model. This study focused on drought drivers, the disaster-forming environment, drought bearers, disaster intensity, and recovery. The results showed that the vulnerability to drought of 42 % of China's provinces decreased from 2010 to 2022, that of only 29 % of the provinces increased, while the status of a majority of provinces improved in general. The center of gravity of the vulnerability to drought moved toward the southwest over time and a spatial clustering of vulnerability was observed, with High-High clusters moving from the north to the south. Moreover, the resilience to drought declined in 36 % of provinces and increased in only 20 %, reflecting poor resilience overall. The center of gravity of China's overall resilience to drought moved northward, with a relatively stable spatial pattern and prominent clusters of Low-Low resilience indicating a pressing need for improvement. Areas with high vulnerability and low resilience were concentrated in inland western and eastern regions, and this highlights the importance of drought prevention and mitigation in provinces like Xinjiang, Inner Mongolia, Jiangxi, and Fujian. The findings here provide valuable insights for mitigating the risk of drought and promoting sustainable socioeconomic development.

Temporal and Spatial Distribution Characteristics of Torrential Flooding Disasters with Different Intensities in Tarim Basin

As torrential flooding often cause huge economic losses and casualties, analyzing the spatio-temporal variation characteristics of torrential flooding events is of great significance to disaster prevention and reduction. Based on five indicators for torrential floodingin the Tarim Basin in 1990-2019, ratio weighting and non-dimensional linear summation were employed to calculate disastrous loss indicators that represent disaster intensity. Afterwards, percentile method was used to divide disasters into four levels, i.e., general, relatively severe, severe and extremely severe. The results showed that the regions where Level-1 to Level-4 disasters frequently and recurrently occur are concentrated in Kizilsu Kirghiz Autonomous Prefecture, Aksu Prefecture and Kashgar Prefecture and that such disasters often take place from April to July. The interannual variation of the frequency and intensity of Level-1 disasters presented a linear upward trend, and the frequency and disastrous loss indicator increased by 14.6 and 0.8 per 10a, respectively. The interannual variation of the frequency and intensity of Level-2 to Level-4 disasters did not show a linear increase or decrease trend. The threshold for 12-hour precipitation that may cause torrential flooding in the basin from March and October is 10mm. The annual frequency of 12-hour precipitation exceeding the threshold increased year by year, so did the frequency and intensity of Level-1 disasters.

Does Economic Growth Increase or Decrease Economic Losses and Deaths from Storm Surge Disasters in China? An Empirical Analysis from The Aspect of Hazard Exposure

Storm surge disaster is the major threat to coastal residents’ lives and property in China, which could steal away development gains in a short time. Therefore, the storm surge disaster loss reduction is an indispensable part of socio-economic development. However, the relationship between economic growth and the loss of storm surge disasters remains unclear. Herein, we explored the relationship between economic growth and the losses caused by storm surge disasters in China’s coastal zones from 1978 to 2020. We established an empirical model through the indicators of direct economic loss and death toll using negative binomial estimate and tobit estimate models. To ensure the accuracy of the model, multiple socioeconomic factors were also included in the models. The results revealed that there is an “inverted U-shaped” relationship between GDP per capita and the losses from storm surges. Furthermore, we used the entropy method to establish the storm surge hazard exposure index of China’s coastal zone from 2000 to 2020, considering disaster intensity, population density and other factors. When considering hazard exposure level, the role of economic growth may be diminished. In other words, at the same economic level, higher hazard exposure leads to greater disaster losses. We argue that economic growth and disaster resilience are not identical goals. Thus, the government needs sufficient information support when formulating disaster prevention and reduction plans.

A REVIEW OF STOCK MARKET IMPACT OF ENVIRONMENTAL CATACLYSMS AND LESSONS FOR INVESTORS

The objective of this paper is to present a review analysis of stock market impact of environmental disaster with the factors that orchestrate the impact, and to offer a novel framework for investors and managers. The paper applied an in-depth empirical review and synthesis method. Findings from the review and synthesis of empirical literature provide strong evidence that environmental disasters have diverse effects on the stock markets with majority of the literature reporting adverse effects on the stock markets. Additionally, findings are more preponderant on short run impact than on long run effects. Findings also show that environmental disasters may offer unique arbitrage opportunities for short-selling investors. The paper concludes by highlighting the capacity of environmental finance as one of the catalysts to bolster corporate adaptive capacity to environmental disaster. Furthermore, the degree of environmental disaster impact on stock market may depend on factors such as the type of environmental disaster, geographical location of environmental disaster, economic sector, stage of economic development, length of disaster and intensity of disaster. Accordingly, the paper develops a framework, which is recommended as a model for further research agenda on environmental disaster effect on stock markets.

Risk Assessment of World Corn Salinization Hazard Factors Based on EPIC Model and Information Diffusion

Salinization is a serious land degradation phenomenon. This study identified the salinity stress threshold as a causal factor for salinization, focusing on global maize fields as the study area. By excluding environmental stressors and setting salinization scenarios, the EPIC model was used to simulate the daily salinity stress threshold during the corn growth process. The global intensity and risk of salinization-induced disaster for maize were evaluated. Based on the principle of information diffusion, the intensity of salinization-induced disaster was calculated for different return periods. The main conclusions were as follows: (1) By excluding environmental stress factors and setting salinization scenarios, algorithms for the salinization index during the growing season and the intensity of salinization-induced disaster were proposed. (2) The salinity hazard factor is highly risky and concentrated in arid and semi-arid regions, while it is relatively low in humid regions. (3) As the recurrence period increases, the risk of salinization-induced hazard becomes higher, the affected area expands, and the risk level increases. (4) The salinization intensity results of this study are consistent with the research results of HWSD (R2 = 0.9546) and GLASOD (R2 = 0.9162).

An intelligent power grid emergency allocation technology considering secondary disaster and public opinion under typhoon disaster

Typhoon disaster may not only cause power outage physically, but also breed negative sentiments in public opinion and affect social stability. Therefore, an intelligent power grid allocation technology considering secondary disaster and public opinion under typhoon disaster is proposed. Firstly, an Extra-Tree (ET) method is used to predict the power outage in time sequence (e.g., landing period, 6 h later, and 12 h later) after typhoon lands. Secondly, a typhoon secondary disaster potential assessment model is established based on disaster intensity and geographical environment. Since power outages may also cause public opinion polarization, a public opinion analysis model is proposed to mine Micro-blog (similar to Twitter) tweet information and analyze the demand for power restoration. Then, according to the results of secondary disaster potential assessment and public opinion analysis, the allocation strategy optimization is described as a Mixed Integer Nonlinear Programming (MINLP) problem. It is solved by the Stud Genetic Algorithm (Stud GA) and A-star path optimization algorithm. Finally, Yangjiang city in China under Typhoon “Chaba” (2022) is selected as the study area to verify the effectiveness and feasibility. It shows the proposed method can respond to public electricity demand in time and eliminate public negative sentiments, while the repair process is not disturbed by secondary disasters.

냉동창고 건설공사 화재예방시스템 개선에 관한 연구

The domestic logistics environment must provide services in real time, regardless of time and place, due to increased delivery volume, increased logistics warehouse functions, increased delivery speed, increased low-temperature products, and the 4th Industrial Revolution. In order to survive in this logistics environment, logistics companies and others are pursuing profits on a large scale and short construction periods as business survival strategies, so the exposure of risks to logistics warehouse construction is very serious. Among them, more than 1,000 fire accidents with very serious disaster intensity have continued to occur over the past 10 years since 2010, and fire accidents have caused dozens of lives and property damage of tens of billions of won every year. The purpose of this study is to reveal the problems of the fire prevention system of the frozen warehouse construction work through case analysis and related laws and literature that occurred in the construction of the frozen warehouse among the logistics warehouses and to suggest ways to improve them.
 The research method was to investigate and analyze fire status, accident cases (centered on accidents at frozen warehouses in Icheon, Gyeonggi-do in 2008), theoretical consideration such as 4M techniques, and suggest ways to improve the fire prevention system.
 Fire prevention mechanisms of 4M refrigeration warehouse construction were investigated. Fire control by unqualified firemen, fire monitors, and lack of management by supervisors existed mechanically. The construction method of fire prevention was improved S.H.P(Safety Hold Point) operation, replacement of frozen warehouse insulation with semi-non-combustible urethane foam, introducing a mandatory fire prevention education system, strengthening emergency simulation training, and finally operating a one-out system for those who do not fulfill fire prevention guidelines.
 While conducting this study, it was an opportunity to examine the seriousness of fire accidents in frozen warehouse construction work, and I hope that the measures proposed through this study will help prevent fire in frozen warehouse construction work.

Estimating sustainability and regional inequalities using an enhanced sustainable development index in China

Accelerating warming has led to intensified natural disasters and a lower margin for greenhouse gas emissions, affecting both development sustainability and regional inequalities. However, the impacts have seldom been studied, especially in fast-developing countries with large annual CO2 emissions and frequent agricultural disasters, such as China. We have developed an enhanced sustainable development index (ESDI), which retains the base formula of the sustainable development index (SDI) but restructures the ecological component, replacing the material footprint (MF) with agricultural disaster intensity. The modification avoids overestimation of carbon emission impacts and incorporates a new dimension into sustainability estimation. The temporal variabilities and spatial imbalance of the China ESDI were characterized in 1997-2018 using linear trends, hot spots, the Thiel index, and variation coefficient analysis. The results showed 1) an overall ESDI decrease in China with differences between northern and southern half China (north decreasing and south increasing) and 2) the spatial inequities of development sustainability, mainly arising from the northern half China, continued to increase. The low sustainability clustered in the northern half, and the high sustainability clustered in the southern half. Correspondingly, the development sustainability of the former was more imbalanced than that of the latter. 3) The dramatic CO2 emissions increase since the late 2000s mainly accounts for the ESDI decrease in all of China and the northern half of China and is responsible for the enlarging sustainability imbalance as well.

The impact of tropical cyclones on income inequality in the U.S.: An empirical analysis

Despite a growing number of studies on the effects of tropical cyclones on economic growth, not much is known about their effects on income inequality. This paper addresses this question by using a panel data set of U.S. counties affected during the period 2010–2019. It exploits geophysical information to construct the disaster intensity predictor, and shows that storm shocks significantly decrease local income inequality levels in the year that they occur. A close study of the composition effects reveals several interesting patterns. First, the immediate impact is driven by the two lowest quintiles and the top quintile of the income distribution. Second, the beneficial effects on income inequality are stabilised in the short-run and the frequency of events is a crucial parameter to consider as repeatedly exposed counties exhibit larger responses after a cyclone strike than their rarely exposed counterparts. Finally, among others, this study stresses the importance of social safety nets in counteracting tropical cyclones’ adverse income distributional effects.

A disaster-damage-based framework for assessing urban resilience to intense rainfall-induced flooding

Resilience has been widely used as a concept to analyse, understand, and improve cities' coping capacities to disasters. However, it is still a challenge to operationalise and quantify resilience. This study proposes a framework for assessing resilience to disasters based on the relationship between disaster intensity and damage rate. We use intense (short-term heavy) rainfall-induced urban flooding in Shenzhen city, one of the largest cities in China, as an example to explore the main features and transferability of the proposed resilience assessment framework. In addition, we demonstrate the usability of the proposed framework by using it to assess and compare the effectiveness of two resilience-building strategies: (1) permeable pavement transformation and (2) land vulnerability reduction. This research makes an innovative contribution through its effective disaster-damage-based approach for quantitatively evaluating urban resilience to disasters, which can support building resilience and mitigating the impact of climate change.

Community Climate Vulnerability Assessment (CCVA) to Climate Change of a Hilly Area of Rangamati District in Bangladesh

The global challenge of Climate change poses a significant threat to humanity, with its impacts already being felt in various parts of the world through unpredictable and severe weather events leading to property damage and loss of life (IPCC, 2014). Community resilience is vital in reducing the losses caused by climate change, and it depends on an understanding of perceived risks, vulnerabilities, and local efforts to mitigate them. The Chittagong Hill Tracts of Bangladesh is one of the regions that is particularly vulnerable to climate change, due to its geography, degradation of forests, sensitivity of livelihoods, and low capacity in comparison to other parts of the country (World bank 2018).Although vulnerability assessments to climate change have been conducted previously, there remains a gap in involving indigenous communities residing in hilly areas of Bangladesh. The recognition of climate change risks as perceived by local communities could serve as a foundation for developing locally-led adaptation plans aimed at building local resilience.The Basonto Mon watershed is located in Rangamati district of Bangladesh, with GPS coordinates of 22˚ 40.218"N 92˚ 16.620" E (GPS coordinates derived from Google Earth) and an elevation of 390 feet above sea level. The area comprises of five villages from varying elevations and is primarily inhabited by the Chakma community. There are 269 households in the region, primarily relying on Jhum cultivation, agriculture on fringe lands, and seasonal labor for their livelihoods, with an average annual household income of BDT 96000 (approximately US$ 1132). About 76% of the community consider themselves under serious threat from the effects of climate change, while 24% have limited understanding of the issue. Approximately 20% of the respondents believe that their livelihoods will be mostly affected by climate change, while 78% identified multiple impacts including health, disaster intensity, family workload, and more. The region is also facing severe soil erosion caused by deforestation, incorrect agricultural practices, and the monoculture of forest species such as teak. The community is at risk from agricultural drought, flash floods, landslides, cyclones, among other things, with the most impacted months being March to August.Climate drivers in the area include erratic rainfall, sudden heavy downpours, increased number of rainless days, and rising temperatures. Acute water scarcity caused by the drying of streams is another major concern for the community and has a widespread impact on women and their families. The most vulnerable sectors identified are the forest and ecosystem, livelihoods, and water security.Approximately 75% of the population is literate and has access to educational institutions and health clinics. However, the environment is fragile. To address this, there are three main priorities: 1) preserving the forests through cooperative efforts, reforestation, and public education, 2) promoting sustainable and climate-resistant agriculture, as well as alternative livelihoods and market access, and 3) improving access to clean water and preserving water sources through community-led management. Key stakeholders, such as the CHT institutions and local government bodies, are crucial in supporting the community in becoming more resilient to the effects of climate change.

500-Year Records Demonstrating a Sharp Increase in Intensities of Three Natural Hazards at Multiple Spatiotemporal Scales in China

China has experienced frequent natural disasters, including droughts, floods, and heavy snowfall. This study discusses the temporal-spatial patterns in the country since 1500. The intensity of drought in Henan and Inner Mongolia was higher than that in Guizhou and Qinghai, while little difference in flood intensity was observed among these provinces. The intensity of snow disasters in Qinghai was much higher. Except for the slightly decreasing drought trend in Henan, the three natural disasters showed a significant increase over time. Drought disasters in Guizhou, Henan, and Qinghai showed few seasonal differences, whereas those in Inner Mongolia mostly occurred in winter and spring. Floods were concentrated during the summer, while snow disasters occurred mainly during winter and spring. According to the Mann–Kendall (M–K) test, the seasonal differences in disaster trends in Guizhou, Henan, and Qinghai were unclear and similar to the overall trend. However, the disaster trends in Inner Mongolia showed seasonal differences. The spatial distribution of natural disasters in Guizhou and Henan were similar, and their changing trends were extremely scattered, while in Inner Mongolia and Qinghai, they were clustered. The spatial distribution of disaster intensity had few seasonal differences and was similar to the overall distribution. However, when considering the spatial distribution of disaster trends by season, seasonal differences were evident. This study has provided an earlier signal on how to prevent and mitigate natural disasters based on 500 years' tempo-spatial pattern, and the measures on how to improve the management practices of natural hazards under climate change were also suggested.

Performance Evaluation of VoIP Service for Emergency Disaster Responses over WiMAX-WLAN Integrated Network

As a very large archipelago country, Indonesia has a high intensity of the natural disaster. In many cases, the telecommunication infrastructure collapse during the disaster. Therefore, a prominent and robust emergency telecommunication network is required. In this paper, an implementation of VoIP services over the WiMAX-WLAN integrated network is proposed. The integrated network is designed to provide an alternative of the emergency network during the disaster response. The VoIP service is selected for the study case because this service can be used as a complementary of the telephony service. In this paper, the WiMAX network is designed based on the IEEE Standard 802.16-2004 with the frequency band of 3.5 GHz while the WLAN network is designed based on the IEEE Standard 802.11n. To support many scenarios of network topology, the proposed integrated network is designed in P2P and PMP topologies. Within these network topologies, the network performance is evaluated in terms of delay, jitter, throughput and packet loss, as well as MOS value. The network performance is evaluated over various background traffic. From the experimental evaluation, it shows that the network becomes worse when the network load that is represented by the background traffic exceeding 3 Mbps. In general, there is no significant performance different between P2P and PMP network topology.

A coupled multi-model framework for waterlogging projection: Towards achieving sustainable development goal 11.5

Modeling potential changes in flooding from urbanization enhances urban risk perception and supports decision-makers in preventative actions of high-risk areas. This study proposes a coupled multi-model framework for projecting the distribution and severity of urban floods and tests for Shenzhen City in China under multi-scenarios. The results show that the coupled multi-model framework has a high accuracy level at 78%. In Shenzhen, the flood-prone areas are more frequently found in the west and less frequently in the east, while the valley regions in the southeast are vulnerable to deep waterlogging. The growth rate of the inundated region slows down as the inundation depth rises after rainfall intensity exceeds that of the 20-year event. The projected results show that the flooding area and waterlogging disaster intensity (WDI) in each watershed in 2035 would be larger those in 2015. In the basins of Chi'ao Reservoir, Ejing Reservoir, and Guangming Farm in the Maozhou River midstream watersheds, the risk of the flood area expanding would be greater than the risk of the flood deepening. The findings provide a spatial reference for flood control and strategic urban drainage design and facilitate the achievement of sustainable development goal 11.5 (SDG11.5) in Shenzhen and other regions.