Ecological Risk Assessment of Geological Disasters Based on Probability-Loss Framework: A Case Study of Fujian, China.

Geological disaster could pose a great threat to human development and ecosystem health. An ecological risk assessment of geological disasters is critical for ecosystem management and prevention of risks. Herein, based on the "probability-loss" theory, a framework integrating the hazard, vulnerability, and potential damage for assessing the ecological risk of geological disasters was proposed and applied to Fujian Province. In the process, a random forest (RF) model was implemented for hazard assessment by integrating multiple factors, and landscape indices were adopted to analyze vulnerability. Meanwhile, ecosystem services and spatial population data were used to characterize the potential damage. Furthermore, the factors and mechanisms that impact the hazard and influence risk were analyzed. The results demonstrate that (1) the regions exhibiting high and very high levels of geological hazard cover an area of 10.72% and 4.59%, respectively, and are predominantly concentrated in the northeast and inland regions, often distributed along river valleys. Normalized difference vegetation index (NDVI), precipitation, elevation, and slope are the most important factors for the hazard. (2) The high ecological risk of the study area shows local clustering and global dispersion. Additionally, human activities have a significant influence on ecological risk. (3) The assessment results based on the RF model have high reliability with a better performance compared with the information quantity model, especially when identifying high-level hazard areas. Our study will improve research on the ecological risk posed by geological disasters and provide effective information for ecological planning and disaster mitigation.

Multi-Disaster Integrated Risk Assessment in City Range-A Case Study of Jinan, China.

Urban multi-disaster integrated risk assessment is an important part of urban sustainable development and territorial spatial planning. Based on the results of integrated risk assessment, the scientific and effective performance of disaster prevention and reduction can be effectively improved. This study determines a multi-disaster integrated risk assessment system. The system evaluates the hazard level of disasters, the exposure level of disaster bearing bodies, the vulnerability level of disaster bearing bodies, and the urban resilience level, and determines the city's integrated risk level on this basis. Taking Jinan as an example, the risk, exposure, vulnerability, resilience, and integrated risk level of Jinan City were analyzed. The results show that the system reasonably analyzes the multi-disaster integrated risk level, and according to the assessment results, countermeasures for disaster prevention and suggestions for territorial spatial planning were put forward.

An Integrated Artificial Intelligence of Things Environment for River Flood Prevention.

River floods are listed among the natural disasters that can directly influence different aspects of life, ranging from human lives, to economy, infrastructure, agriculture, etc. Organizations are investing heavily in research to find more efficient approaches to prevent them. The Artificial Intelligence of Things (AIoT) is a recent concept that combines the best of both Artificial Intelligence and Internet of Things, and has already demonstrated its capabilities in different fields. In this paper, we introduce an AIoT architecture where river flood sensors, in each region, can transmit their data via the LoRaWAN to their closest local broadcast center. The latter will relay the collected data via 4G/5G to a centralized cloud server that will analyze the data, predict the status of the rivers countrywide using an efficient Artificial Intelligence approach, and thus, help prevent eventual floods. This approach has proven its efficiency at every level. On the one hand, the LoRaWAN-based communication between sensor nodes and broadcast centers has provided a lower energy consumption and a wider range. On the other hand, the Artificial Intelligence-based data analysis has provided better river flood predictions.

Facing flood disaster: A cluster randomized trial assessing communities' knowledge, skills and preparedness utilizing a health model intervention.

Floods occur when a body of water overflows and submerges normally dry terrain. Tropical cyclones or tsunamis cause flooding. Health and safety are jeopardized during a flood. As a result, proactive flood mitigation measures are required. This study aimed to increase flood disaster preparedness among Selangor communities in Malaysia by implementing a Health Belief Model-Based Intervention (HEBI). Selangor's six districts were involved in a single-blinded cluster randomized controlled trial Community-wide implementation of a Health Belief Model-Based Intervention (HEBI). A self-administered questionnaire was used. The intervention group received a HEBI module, while the control group received a health talk on non-communicable disease. The baseline variables were compared. Immediate and six-month post-intervention impacts on outcome indicators were assessed. 284 responses with a 100% response rate. At the baseline, there were no significant differences in ethnicity, monthly household income, or past disaster experience between groups (p>0.05). There were significant differences between-group for intervention on knowledge, skills, preparedness (p<0.001), Perceived Benefit Score (p = 0.02), Perceived Barrier Score (p = 0.03), and Cues to Action (p = 0.04). GEE analysis showed receiving the HEBI module had effectively improved knowledge, skills, preparedness, Perceived Benefit Score, Perceived Barrier Score, and Cues to Action in the intervention group after controlling the covariate. Finally, community flood preparedness ensured that every crisis decision had the least impact on humans. The HEBI module improved community flood preparedness by increasing knowledge, skill, preparedness, perceived benefit, perceived barrier, and action cues. As a result, the community should be aware of this module. Clinical trial registration: The trial registry name is Thai Clinical Trials Registry, trial number TCTR20200202002.

Comparative Study on International Research Hotspots and National-Level Policy Keywords of Dynamic Disaster Monitoring and Early Warning in China (2000-2021).

For more than 20 years, disaster dynamic monitoring and early warning have achieved orderly and sustainable development in China, forming a systematic academic research system and top-down policy design, which are inseparable from the research of China's scientific community and the promotion of government departments. In the past, most of the research on dynamic disaster monitoring and early warning focused on specific research in a certain field, scene, and discipline, while a few studies focused on research review or policy analysis, and few studies combined macro and meso research reviews in academia with national policy analysis for comparative analysis. It is necessary and urgent to explore the interaction between scholars' research and policy deployment, which can bring theoretical contributions and policy references to the top-down design, implementation promotion, and academic research of China's dynamic disaster monitoring and early warning. Based on 608 international research articles on dynamic disaster monitoring and early warning published by Chinese scholars from 2000-2021 and 187 national policy documents published during this period, this paper conducts a comparative analysis between the knowledge maps of international research hotspots and the co-occurrence maps of policy keywords on dynamic disaster monitoring and early warning. The research shows that in the stage of initial development (2000-2007), international research articles are few and focused, and research hotspots are somewhat alienated from policy keywords. In the stage of rising development (2008-2015), after the Wenchuan earthquake, research hotspots are closely related to policy keywords, mainly in the fields of geology, engineering disasters, meteorological disasters, natural disasters, etc. Meanwhile, research hotspots also focus on cutting-edge technologies and theories, while national-level policy keywords focus more on overall governance and macro promotion, but the two are gradually closely integrated. In the stage of rapid development (2016-2021), with the continuous attention and policy promotion of the national government, the establishment of the Ministry of Emergency Management, and the gradual establishment and improvement of the disaster early warning and monitoring system, research hotspots and policy keywords are integrated and overlapped with each other, realizing the organic linkage and mutual promotion between academic research and political deployment. The motivation, innovation, integration, and transformation of dynamic disaster monitoring and early warning are promoted by both policy and academic research. The institutions that issue policies at the national level include the State Council and relevant departments, the Ministry of Emergency Management, the Ministry of Water Resources, and other national ministries and commissions. The leading affiliated institutions of scholars' international research include China University of Mining and Technology, Chinese Academy of Sciences, Wuhan University, Shandong University of Science and Technology, and other institutions. The disciplines involved are mainly multidisciplinary geosciences, environmental sciences, electrical and electronic engineering, remote sensing, etc. It is worth noting that in the past two to three years, research and policies focusing on COVID-19, public health, epidemic prevention, environmental governance, and emergency management have gradually increased.

Factors Influencing Public Participation in Community Disaster Mitigation Activities: A Comparison of Model and Nonmodel Disaster Mitigation Communities.

Public participation in community-organized disaster mitigation activities is important for improving disaster mitigation capacity. With data from 260 questionnaires, this study compared the current status of public participation in model disaster mitigation communities and nonmodel communities in a geological-disaster-prone area. Three community-organized disaster mitigation education activities were compared cross-sectionally. A binary logistic regression was used to analyze the effects of attitude, perceived behavioral control, disaster experience, and other key factors on the public's choice to participate in community disaster mitigation activities. The analysis results indicated that model communities had higher public participation in two efforts, evacuation drills and self-help skills training, and lower participation in activities that invited them to express their feedback than nonmodel communities. The influence of attitudinal factors on the decision to participate in disaster mitigation activities had a high similarity across community types. The public participation in model disaster mitigation communities is influenced by factors such as subjective norms and participation cognition; the behavior of people in nonmodel communities is influenced by factors such as previous experience with disasters, perceived behavioral control, risk perception, and participation cognition and has a greater potential for disaster mitigation community construction. This study provides practical evidence and theoretical support for strengthening the sustainable development of disaster mitigation community building.

[GIS-based risk assessment of agrometeorological disasters of summer maize in northern mountainous area of Sichuan Basin: A case study of Wangcang County, Sichuan Province, China]. / 基于GIS的四川盆周北部山区夏玉米农业气象灾害风险评估­­ä»¥æ—ºè‹åŽ¿ä¸ºä¾‹.

Meteorological disaster is one of the main factors restricting agriculture development in China. It is important to clarify the risk of summer maize agrometeorological disaster for disaster prevention and mitigation. Based on the natural disaster risk theory, we collected meteorological data and maize yield data from 1981 to 2018 in a typical area in the northern mountainous area of Sichuan Basin (Wangcang County). The main disaster-causing factors affecting summer maize production were determined. A comprehensive agro-meteorological disaster risk assessment model for summer maize was constructed in combination with the sensitivity of pregnant environment and vulnerability of disaster bearing to evaluate the agrometeorological disasters risk of summer maize production in the northern mountainous area of Sichuan Basin. The results showed that during the study period, high temperature in mature period, rainstorm in flowering period, rainstorm in mature period, continuous rain in filling period and drought in booting stage were the main agrometeorological disasters affecting the growth and development of summer maize. The agrometeorological disaster risk of maize generally distributed in a southwest-northeast pattern, with the distribution areas of high-risk and higher-risk areas accounting for half of the total area of Wangcang County. The high-risk areas were mainly located in the southwest of the county, which was basically consistent with the high-value areas of hazard-pregnant environment sensitivity. The low-risk areas were mostly concentrated in the western part of county territory, which were also low-risk areas of high temperature in mature period, rainstorm in mature period, and rainstorm in flowering period disasters.

The preparedness of disaster among nurses in community health centers in rural areas during the COVID-19 pandemic in Malang City.

Positive COVID-19 cases in Malang City, Indonesia continue to increase. Until 04 August 2021, the COVID-19 update shows 3301 positive cases with 7754 cured and 832 deaths. This study aims to identify nurses preparedness in rural area community health centers during the COVID-19 pandemic in Malang for self-control to implement health protocol. This study intends to provide insights on controlling COVID-19 spread in Malang, Indonesia. This research is a quantitative study with correlative analytic observational design and a cross-sectional approach involving 120 nurses from 16 primary health centers. The results of the bivariate analysis using gamma correlation test are: knowledge factors (p = 0.005; r = 0.35), attitude (p = 0.000; r = 0.46), means of infrastructure (p = 0.000; r = 0.54), and self-control (p = 0.000; r = 0.52) for the quarantined COVID-19 patients. Knowledge, attitude, infrastructure, and safe house factors can influence self-control for COVID-19. In rural areas, health education-as education and empowerment for patient self-control-is an effort to encourage them to obey health protocol during the pandemic. Nurse readiness and preparedness during the pandemic is crucial for strengthening the assertive behavior commitment through self-control. This ensures the community's awareness of the importance of complying with health protocols for the common good. Mental nursing intervention needs to be added as a part of psychosocial therapy for the community's social problems, primarily in reducing the pressure due to the social distancing enforcement to control and prevent COVID-19 spread.

Exploring disaster impacts on adaptation actions in 549 cities worldwide.

Whether disasters influence adaptation actions in cities is contested. Yet, the extant knowledge base primarily consists of single or small-N case studies, so there is no global overview of the evidence on disaster impacts and adaptation. Here, we use regression analysis to explore the effects of disaster frequency and severity on four adaptation action types in 549 cities. In countries with greater adaptive capacity, economic losses increase city-level actions targeting recently experienced disaster event types, as well as actions to strengthen general disaster preparedness. An increase in disaster frequency reduces actions targeting hazard types other than those that recently occurred, while human losses have few effects. Comparisons between cities across levels of adaptive capacity indicate a wealth effect. More affluent countries incur greater economic damages from disasters, but also have higher governance capacity, creating both incentives and opportunities for adaptation measures. While disaster frequency and severity had a limited impact on adaptation actions overall, results are sensitive to which disaster impacts, adaptation action types, and adaptive capacities are considered.

Agricultural Disaster Risk Management and Capability Assessment Using Big Data Analytics.

Besides many impacts, climate change and the rise of harsh weather have a huge hit that jeopardizes agricultural sectors. Natural catastrophes, including flooding and wildfires, are the sources of significant declines in crop production. National governments make an essential commitment, and foreign institutions work together to mitigate disasters' resilience vulnerability. These hazards have pushed catastrophe management to the forefront and made it an expanding scholarly area of study. The remarkable growth of information technology has motivated the scientific group to integrate this technology into emergency management. In this article, agricultural disaster risk management (ADRM) is offered to decide the status quo of the research on agriculture disaster management and the significance of big data. This article's primary objective is to provide technical metric analysis to analyze the body of research carried out in the past decade on different forms of disasters and the use of significant volumes. For the data assessment, the annual growth of publication outcomes, the corresponding categories of topics, and the productivity study specifications was determined. The flux of raw and analytical data from comprehensive data is so established that another effect is heavily affected in the final performance of forecasting. The assessment of ADRM proposed would have been based on data provided by the Department of Indian Meteorology, and improvement is illustrated in incorporating the mechanism proposed in flood prediction long before the occurrence of floods.

Social Stability Risk Assessment of Disaster-Preventive Migration in Ethnic Minority Areas of Southwest China.

Disaster-preventive migration (DPM) is an important method for disaster risk management, but migration itself entails a potential social stability risk. This study took County D in Yunnan Province, one of the counties most severely threatened by geological disasters in China, as an example to construct an indicator system of social stability risk factors for disaster-preventive migration based on a literature survey and in-depth interviews. The system consists of 5 first-level risk factors and 14 s-level risk factors. The social stability risk of DPM in County D was assessed using a fuzzy comprehensive evaluation method based on experts' weights. The results showed that the overall social stability risk level of disaster-preventive migration in County D is 'high'. In terms of importance, the five first-level risk factors were ranked as follows: public opinion risk > compensation risk > livelihood recovery risk > cultural risk > geological disaster risk. Among the risk factors, the level of public opinion risk and compensation risk appeared to be high, whereas that of livelihood recovery risk, cultural risk and geological disaster risk resulted to be medium. To our knowledge, this paper is the first research to evaluate the social stability risk of DPM; it not only enriches the theories of social stability risk assessment, but also has important guiding significance for people relocation and resettlement in Chinese ethnic minority areas.

Rice Drought Damage Assessment Using AMSR-E Data Inversion of Surface Temperature.

The extreme drought events caused by global warming have become one of the major issues of general concern all over the world. It is estimated that over the past 50 years, the average annual drought-affected area has reached more than 200,000 km2, resulting in a global economic loss of US$6-8 billion, far exceeding other meteorological disasters. Therefore, conducting real-time and effective drought monitoring research is of great significance for issues such as climate change, drought defense, water resources management, and protection in various regions. Rice is the largest food crop in China and plays a pivotal role in food production. Drought is often regarded as one of the most important stress factors. Scientific, accurate, and timely assessment of the impact of drought on rice yield is essential for improving crop drought resistance and ensuring food production. In this study, based on the meteorological data, rice growth period and yield of the main rice planting areas in Chongqing Yangtze River Basin, and based on the drought index of passive microwave remote sensing observation data (AMSR-E), a statistical model of rice meteorological yield and drought index under the influence of drought is established. A rice drought disaster assessment is carried out. The results of the disaster assessment indicate that under the influence of drought, the rice yield reduction rate of representative sites in Chongqing Yangtze River Basin is between 3% and 10%.

A hybrid novel framework for flood disaster risk control in developing countries based on smart prediction systems and prioritized scenarios.

A Decision Support System (DSS) is a highly efficient concept for managing complex objects in nature or human-made phenomena. The main purpose of the present study is related to designing and implementation of real-time monitoring, prediction, and control system for flood disaster management as a DSS. Likewise, the problem of statement in the research is correlated to implementation of a system for different climates of Iran as a unique flood control system. For the first time, this study coupled hydrological data mining, Machine Learning (ML), and Multi-Criteria Decision Making (MCDM) as smart alarm and prevention systems. Likewise, it created the platform for conditional management of floods in Iran's different clusters of climates. According to the KMeans clustering system, which determines homogeneity of the hydrology of a specific region, Iran's rainfall is heterogeneous with 0.61 score, which is approved high efficiency of clustering in a vast country such as Iran with four seasons and different climates. In contrast, the relation of rainfall and flood disaster is evaluated by Nearest Neighbors Classification (NNC), Stochastic Gradient Descent (SGD), Gaussian Process Classifier (GPC), and Neural Network (NN) algorithms which have an acceptable correlation coefficient with a mean of 0.7. The machine learning outputs demonstrated that based on valid data existence problems in developing countries, just with verified precipitation records, the flood disaster can be estimated with high efficiency. In the following, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method as a Game Theory (GT) technique ranked the preventive flood damages strategies through three social (Se 1), environmental (Se 2), and economic (Se 3) crises scenarios. The solutions of flood disaster management are collected from literature review, and the opinion approves them of 9 senior experts who are retired from a high level of water resource management positions of Iran. The outcomes of the TOPSIS method proved that National announcement for public-institutional participation for rapid response and funding (G1-2), Establishment of delay structures to increase flood focus time to give the animals in the ecosystem the opportunity to escape to the upstream points and to preserve the habitat (G 2-8), and Granting free national financial resources by government agencies in order to rebuild sensitive infrastructure such as railways, hospitals, schools, etc. to the provincial treasury (G3-10) are selected as the best solution of flood management in Social, Environmental, and Economic crises, respectively. Finally, the collected data are categorized in Social, Environmental, and Economic aspects as three dimensions of Sustainable Development Goals (SDGs) and ranked based on the opinion of 32 experts in the five provinces of present case studies.

Evaluation method on seismic risk of substation in strong earthquake area.

Frequent earthquakes in strong earthquake areas pose a great threat to the safety operation of electric power facilities. There exists a pressing research need to develop an assessment method for the seismic risk of substations, i.e., the hubs of power system networks. In this study, based on Incremental Dynamic Analysis (IDA), Probabilistic Seismic Demand Model (PSDM) and reliability theory, a vulnerability model for a substation is obtained, based on considering the relationships between Peak Ground Acceleration (PGA) and four seismic damage states (complete, extensive, moderate, and slight.) via a probabilistic approach. After an earthquake, the scope of influence and PGA distribution are evaluated using information recorded by the seismic observation stations, based on using interpolation or an empirical formula for the PGA attenuation. Therefore, the seismic risk can be evaluated by combining ground motion evaluation and the pre-built vulnerability model. The Wuqia- Kashgar area of Xinjiang was selected as the study area; it is an Earthquake-prone area, and one of the starting points for new energy transmission projects in China. Under a hypothetical earthquake (MS 7.9), the seismic risk of the substations was evaluated. The results show that: this method is able to give the probabilities of the four damage states of the substations, four substations close to the epicenter only have a probability of slight damage (45%-88%) and other substations are safer.

Approaching the COVID-19 Pandemic Response With a Health Equity Lens: A Framework for Academic Health Systems.

Racially and ethnically diverse and socioeconomically disadvantaged communities have historically been disproportionately affected by disasters and public health emergencies in the United States. The U.S. Department of Health and Human Services' Office of Minority Health established the National Consensus Panel on Emergency Preparedness and Cultural Diversity to provide guidance to agencies and organizations on developing effective strategies to advance emergency preparedness and eliminate disparities among racially and ethnically diverse communities during these crises. Adopting the National Consensus Panel recommendations, the Johns Hopkins Medicine Office of Diversity, Inclusion, and Health Equity; Language Services; and academic-community partnerships used existing health equity resources and expertise to develop an operational framework to support the organization's COVID-19 response and to provide a framework of health equity initiatives for other academic medical centers. This operational framework addressed policies to support health equity patient care and clinical operations, accessible COVID-19 communication, and staff and community support and engagement, which also supported the National Standards for Culturally and Linguistically Appropriate Services in Health and Health Care. Johns Hopkins Medicine identified expanded recommendations for addressing institutional policy making and capacity building, including unconscious bias training for resource allocation teams and staff training in accurate race, ethnicity, and language data collection, that should be considered in future updates to the National Consensus Panel's recommendations.