Natural Disaster Risk Management Research Articles

Collaborative governance and integrated risk management framework of natural disasters

PurposeThis study aims to align the objectives of key stakeholders by developing an integrated framework for high-impact natural disaster risk management. High-impact natural disasters have emerged as one of the most challenging policy issues.Design/methodology/approachThe authors have applied the thematic text analysis for analysing the list of questions essential to develop an integrated framework. For theory, the authors have used the theoretical framework of collaborative governance.FindingsThe current work explains how to identify key stakeholders. Furthermore, it describes the framework to fit stakeholders' actions into the actionable components of risk management. Additionally, this framework also helps the firms that fall under the category of the industries “in the proximity of risks” and “the support industries” in modifying their role in the context of natural disaster risk.Research limitations/implicationsThe limitation of this framework is that the authors relied on commonly occurring natural disaster risks to develop the framework. Therefore, risk-specific aspects are less likely to be thoroughly covered in this framework. However, this limitation is not directly impacting the goal of this study. Additionally, in the future more comprehensive framework with the additional element in the existing framework can overcome these limitations.Practical implicationsThe findings of the study offer insights that can be useful for policymakers in developing various preventive strategies. Managers can use the results and align their objectives with policy goals.Social implicationsSocially, if communities try to design local risk management strategies, this framework would be helpful.Originality/valueThis study illustrates the role of objective alignment in high-impact risk management. This study is crucial in extending current knowledge on high-impact natural disaster risk management.

Urban Flooding Disaster Risk Assessment Utilizing the MaxEnt Model and Game Theory: A Case Study of Changchun, China

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development.

Research on Entity Relationships in the Knowledge Graph of Disease Monitoring in Grotto Temples

Abstract. The grotto temple, carved into cliffs and widely distributed, is a significant cultural heritage in China. However, it faces severe damage and collapse threats due to natural disaster risks in its environment. Nearly seventy percent of grotto temples are located in regions prone to earthquakes and water hazards, leading to varying degrees of damage to cultural artifacts. Therefore, preventive measures are necessary to reduce the impact of natural disasters on grotto temples. A knowledge graph, a structured semantic knowledge base describing concepts and their relationships in the physical world, plays a crucial role in knowledge organization and content representation. Entity relationships are the core of knowledge, serving as both foundational data and a key task in constructing knowledge graphs and processing unstructured text. In the field of grotto temple disease monitoring, while data continues to grow, research on the correlation between textual data remains underexplored. This paper adopts the BiLSTM-CRF method to extract entity relationships, matching them with the grotto temple monitoring knowledge graph. Finally, the Neo4j software is utilized to program and display the knowledge graph, aiming to enhance the efficiency of natural disaster risk management and cultural heritage protection for grotto temples.

Chilean Disaster Response and Alternative Measures for Improvement

Effective DRM aims to identify and minimize both hazards and vulnerabilities of a territory. This case study carried out in Chile analyzes national programs and disaster risk management structures at different administrative levels (national, regional, and municipal) and identifies gaps that contribute to the vulnerability of the current system. The proposed measures and options for improvement presented in this study are based on a literature review of scientific discussions about international governance, disaster risk management, and case studies conducted in Chile. The results indicate that the national disaster risk management plan has been adjusted in recent years, especially after the 2010 Chilean earthquake. The national administration, which is primarily responsible for managing potential risks, as well as the regional and local governments, has been replaced by the National Disaster Prevention and Response System (SINAPRED) in 2021, according to the 21364 law. This law was created to make cities more resilient, contributing to the Sustainable Development Goals (SDGs). This change is intended to decentralize disaster risk management, considering local conditions and preventing oversight of disaster risk management, which is not mandatory at the local level. It has also noted some gaps, such as the lack of standardization of emergency and early warning systems and funding at local levels. It is hoped that the system will move forward in this transition period and that the gaps will not affect effective risk management, as they have caused loss of life in past disasters.

Efundja as a risk driver and change agent for the Cuvelai-Etosha basin rural communities.

Despite the presence of a national disaster risk management strategy, the national disaster response mechanism rather reactively responds to the hazard as opposed to being proactive. Results indicates that the strategy is not fully implemented and the parts that are implemented functions as a top-down approach. Respondents reported a wide range of impacts and a general inability to effectively cope with Efundja, coupled with an absence of their voices in deliberations about risk reduction matters. Additions to the current disaster risk management strategy is proposed and several recommendations derived from the research results concludes the article. Should these recommendations be implemented into the Namibian disaster risk management strategy, Efundja as risk driver will also become an agent of change.

Application of GIS in Natural Disaster Risk Management

The subject of this work is geospatial analysis in the function of natural disaster risk management. GIS analysis can be applied in predicting the consequences of possible natural disasters, as well as in the case of eliminating damage caused by natural disasters that have already occurred. Analyses in this project were carried out using freely available geodata from the Internet.

Probabilistic Approach to Transient Unsaturated Slope Stability Associated with Precipitation Event

The massif rupture is not always reached under saturated conditions; therefore, the analysis of the unsaturated phenomenon is necessary in some cases. This study performed a probabilistic approach for unsaturated and transient conditions to understand the contribution of physical and hydraulic parameters involved in slope stability. The proposed slope stability model was based on the infinite slope method and a new unsaturated constitutive shear strength model proposed in 2021 by Cavalcante and Mascarenhas. The first-order second-moment method, which incorporated multiple stochastic variables, was used in the probabilistic analysis, allowing the incorporation of seven independent variables for the probability of failure analysis as well as for quantifying the contribution of the variables to the total variance of a factor of safety at any state of moisture. This implementation allows a more realistic estimative for the probability of failure, showing in a practical way the decrease and increase of the probability of failure during a rain event. The model provided promising results highlighting the need to migrate from deterministic analyses to more robust probabilistic analyses, considering the most significant number of stochastic variables. The proposed model helps to understand the influence of moisture content on slope stability, being a possible tool in natural disaster risk management.

A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images

Landslide detection is crucial for natural disaster risk management. Deep-learning-based object-detection algorithms have been shown to be effective in landslide studies. However, advanced algorithms currently used for landslide detection require high computational complexity and memory requirements, limiting their practical applicability. In this study, we developed a high-resolution dataset for landslide-prone regions in China by extracting historical landslide remote sensing images from the Google Earth platform. We propose a lightweight LP-YOLO algorithm based on YOLOv5, with a more-lightweight backbone that incorporates our designed PartitionNet and neck equipped with CSPCrossStage. We constructed and added the vertical and horizontal (VH) block to the backbone, which explores and aggregates long-range information with two directions, while consuming a small amount of computational cost. A new feature fusion structure is proposed to boost information flow and enhance the location accuracy. To speed up the model learning process and improve the accuracy, the SCYLLA-IoU (SIoU) bounding box regression loss function was used to replace the complete IoU (CIoU) loss function. The experimental results demonstrated that our proposed model achieved the highest detection performance (53.7% of Precision, 49% of AP50 and 25.5% of AP50:95) with a speed of 74 fps. Compared to the YOLOv5 model, the proposed model achieved 4% improvement for Precision, 2.6% improvement for AP50, and 2.5% for AP50:95, while reducing the model parameters and FLOPs by 38.4% and 53.1%, respectively. The results indicated that the proposed lightweight method provides a technical guidance for achieving reliable and real-time automatic landslide detection and can be used for disaster prevention and mitigation.

Use of machine learning as a tool for determining fire management units in the brazilian atlantic forest

Geoprocessing techniques are generally applied in natural disaster risk management due to their ability to integrate and visualize different sets of geographic data. The objective of this study was to evaluate the capacity of classification and regression tree (CART) to assess fire risk. MCD45A1 product of the burnt area, relative to a 16-year period (2000-2015) was used to obtain a fire occurrence map, from center points of the raster, using a kernel density approach. The resulting map was then used as a response variable for CART analysis with fire influence variables used as predictors. A total of 12 predictors were determined from several databases, including environmental, physical, and socioeconomic aspects. Rules generated by the regression process allowed to of define different risk levels, expressed in 35 management units, and used to produce a fire prediction map. Results of the regression process (r = 0.94 and r² = 0.88) demonstrate the capability of the CART algorithm in highlighting hierarchical relationships among predictors, while the model's easy interpretability provides a solid basis for decision making. This methodology can be expanded in other environmental risk analysis studies and applied to any area of the globe on a regional scale.

Establishing a National Emergency Medical Team in the Republic of Palau

Background/Introduction:Palau is a small island/large ocean nation in the Pacific with a population of approximately 18,000 and a high level of vulnerability to natural or climate-related disasters. In June 2022, Palau became the smallest nation in the world by population to establish a national Emergency Medical Team (EMT). The team is named “Klemat,” after the rope that holds the sails of Palau’s traditional canoes.Objectives:To establish a national EMT capable of self-sufficient, high-quality clinical response to disasters and outbreaks that may occur locally or in neighboring jurisdictions.Method/Description:The development of Klemat required a multi-step process with support from technical and funding partners. Online orientation webinars and meetings were held in 2019 and 2020, and a national Technical Working Group (TWG) was formed in 2022. A national EMT coordinator was jointly hired by the WHO and the Ministry of Health and Human Services of Palau in 2022, and EMT Standard Operating Procedures were drafted.Results/Outcomes:Klemat is the nation’s deployable clinical response capacity, capable of fully self-sufficient tented out-patient and emergency care in the most austere disaster or outbreak circumstances. There are now 18 fully trained clinical and support team members. Klemat is working with relevant stakeholders to be codified in the national disaster risk management framework of Palau.Conclusion:Palau’s development of a national EMT demonstrates that even the smallest of countries can establish self-sufficient EMTs capable of national or regional response.

Spatial and temporal heterogeneity of tropical cyclone precipitation over China from 1959 to 2018

Tropical cyclone precipitation (TCP) can cause serious floods and urban waterlogs as well as cause various secondary disasters, such as landslides and debris flows, which negatively affect human lives and the sustainable development of the economy. This study applied the prewhitening Mann-Kendall test, empirical orthogonal function, and continuous wavelet transform to investigate the long-term trend, spatiotemporal pattern, and periodicity of TCP at monthly, interannual, and interdecadal timescales over China. The recurrence risks of extreme TCP were analyzed using the return period estimation model. The results showed that 1) TCP displayed a significant increasing trend, especially in eastern China, inland areas, and Guangxi Province. The TCP periodicities were 2.5 and 4.9 years across all of China. However, TCP cycles had large discrepancies in the time and frequency domains in different subregions. 2) Monthly TCP demonstrated a decreasing trend in May and an increasing trend from June to October in all of China. The TCP in northeastern China and southern China tended to decrease in July and August, respectively. 3) TCP demonstrated a decreasing tendency from the 1960s–1980s followed by a rebounding trend in the 1990s–2010s. In addition, TCP showed a dipole mode in the 1970s and 2000s. 4) There was an increasing recurrence risk of extreme TCP in the Yangtze River Delta, Hainan Province, southeastern Guangxi Province, and southwestern Guangdong Province. It is therefore necessary to improve forecasting of extreme TCP events to improve risk management and prevention capacity of natural disasters, especially in regions with high population and economy exposure.

Using business continuity methodology for improving national disaster risk management

AbstractCrises affecting societies at a national level have regularly highlighted the importance of societies being prepared and resilient. There is a lack of standardized and proven procedures to ensure continuity of vital societal functions at a national level, as demonstrated in the recent COVID‐19 pandemic. This article investigates how the methodology of business continuity management (BCM) can be used at a national level to increase resilience and societal safety. The article shows that a societal safety approach, where critical societal functions are based on societal values and basic individual needs, can be a fundamental platform for national continuity management. National risk assessments (NRAs), refer to the process and documentation of assessing risks at a national level. NRAs in three Nordic countries where societal safety is the basis for national disaster risk management (DRM) is analyzed, and in two other countries, which have worked extensively with NRAs. This article presents several possibilities and problems inherent in using the BCM process and methodology for national DRM. Finally, a rough proposal is given on how DRM can be developed at the national level with the help of concepts and methods from BCM that would increase societal safety.

Disaster risk management, or adaptation to climate change? The elaboration of climate policies related to agriculture in Colombia

If climate policies are the result of transnationalization of public policies, how are they reinterpreted at the national level? This study has examined the context in which climate policy is formulated in Colombia from an actor-oriented perspective. Using a traveling model approach,we analyzed the role of actors at international and national levels in raising climate issues and shaping climate policies. The results showed that the translation chains from several governmental and non-governmental actors increase the visibility of climate issues in Colombia and how such consultations help defining and addressing these issues. The analysis arising from the climate traveling model exposes power struggles between competing agencies: the National Planning Department (DNP), which promoted climate-change adaptation, and the National Disaster Risk Management Unit (UNGRD), which favored disaster risk management. At the end of the translation chains, governmental actors re-appropriated the international consultants’ interpretations of the issues and solutions within an economic perspective. Hence, adaptation to climate change came to be seen as an economic opportunity or as a way to avoid economic loss. International dominance in the elaboration of climate policies combined with scant involvement of local actors could constitute a challenge for policy implementation and potentially inhibit achieving successful outcomes.

Psychosocial support by rescuers and volunteers in the circumstances of natural disasters

The potential of natural disasters to cause direct and indirect damage to human health is great. Recognizing the hazards of natural disasters and effective disaster risk management at the national and local levels, as well as a focus on preventive action, are necessary. The efforts of rescue teams (professionals) and volunteers (nonprofessionals) who provide psycho-social assistance in the circumstances of natural disasters should be aimed at re-establishing a satisfactory and acceptable physical, mental, psychological, social and economic condition of the individuals and the community. However, severe events have consequences for the mental health of rescuers and volunteers. Preparing rescuers and volunteers is the initial support activity. Maintaining the psychological well-being of rescuers and volunteers is also achieved through self-care activities. According to the guidelines of the Reference Center for Psychosocial Support, in order to ensure continuous maintenance of the psychological well-being of rescuers and volunteers, the organization that hires them must carry out support activities prior, during and after the response to the accident. In this regard, the way of treatment of rescuers engaged in the protection and rescue system was analyzed.

National Civil Defense and Protection Policy and Municipal Master Plan: Collaboration for Natural Disaster Risk Management in Petrópolis, Brazil

National Civil Defense and Protection Policy and Municipal Master Plan: Collaboration for Natural Disaster Risk Management in Petrópolis, Brazil

Vulnerability curves for masonry buildings affected by hyperconcentrated flows as natural disaster risk management tools for the quantification of material damage

The damage assessment caused by floods, earthquakes, hurricanes among others phenomenons in the world are analyzed with methodologies such as “Vulnerability curves”. In Peru, disasters caused by hyperconcentrated flows are alarming due to a climatic variability such as the “El Niño Costero” phenomenon. Therefore, this research has developed vulnerability curves for 1 and 2-story confined masonry buildings in Urb. San Idelfonso, Ica - Peru; linking the variables: flow depth, associated with the event produced by heavy rains at the top of the “Quebrada Cansas” caused by the “El Niño Costero” phenomenon in 2017, and the percentage of the damage based on the methodology of the United States Army Corps of Engineers (USACE), whose formula is the repair value and total building value. The monetary amounts and items of the buildings are obtained from the RM 415-2017-VIVIENDA of the Ministry of Housing, Construction and Sanitation of Peru. The process consisted of hydrological modeling in HEC-HMS, hydraulic modeling in FLO-2D, damage percentage estimate and vulnerability curves production. Finally, the vulnerability curves for hyperconcentrated flows were contrasted with similar studies regarding curves for flooding and debris flow. The results of the investigation showed that the “El Niño Costero” phenomenon in 2017 had an economic impact of at least 1.3 million soles in Urb. San Idelfonso. In addition, at least 24 buildings had a complete damage and 21 buildings an extensive damage.

Mainstreaming forecast based action into national disaster risk management systems: experience from drought risk management in Kenya

ABSTRACT Drought and food security crises heighten risks to lives and livelihoods in East Africa. In recent years, a shift towards acting in advance of such events has gained momentum, notably among the humanitarian and development community. This shift is premised on tools that link climate forecasts with pre-agreed actions and funding, known as Forecast-based Action (FbA), or anticipatory action more widely. While FbA approaches have been developed by a number of humanitarian agencies, the key to scaling-up is mainstreaming these approaches into national risk management systems. This paper addresses this gap in the context of drought risk management in Kenya. We analyse Kenya's current drought management system to assess the potential usability of climate forecast information within the existing system, and outline steps towards improved usability of climate information. Further, we note the critical importance of enabling institutions and reliable financing to ensure that information can be consistently used to trigger early action. We discuss the implications of this for scaling-up FbA into national risk management systems.

Barriers to Climate Disaster risk Management for Public Health: Lessons from a Pilot Survey of National Public Health Representatives.

This study sought to examine current national disaster risk management capacities, and identify governance barriers to strengthening national preparedness for responding to public health emergencies, associated with the anticipated climate-driven intensification of natural disaster cycles. A mixed-methods online survey, assessing broader governance constraints to climate change adaptation (CCA) for public health, was distributed to representatives of national public health associations, and societies of 82 member countries under the World Federation of Public Health Associations. Specific questions relevant to disaster risk management capacities and barriers were analyzed as part of a narrowed focus on the CCA subdomain of emergency preparedness. Existence of some technology, infrastructure, and/ or human resources, necessary to develop early warning and other surveillance systems for climate-related health risks was reported by 9 out of 11 responding countries. However, 7 reported persistent limitations and/ or regional discrepancies. Most significant identified barriers to strengthening emergency preparedness at the national level included governance coordination challenges, and, in the case of many developing countries, technical, medical, and human resource shortages. The development of new frameworks for intersectoral governance and large-scale resource mobilization will prove crucial to ongoing efforts to strengthen national climate-health resiliency and prepare for disaster-associated health threats.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

Assessment of flood prone zones in the Tarkwa mining area of Ghana using a GIS-based approach

Flood is one of the most imperious natural hazards that can cause economic damages to infrastructures and natural ecosystems. In view of the extensive mining activities in the Tarkwa area, there has been an increase in human intervention of natural water bodies via degradation of vegetation in the watershed area, inappropriate land-use change, soil erosion and expansion of paved surface area for settlement and industrialisation. This has increased the degree of flood susceptibility within the area, with major flood incidents recorded on a timely basis. In this study, a GIS-based approach was utilised to delineate flood-prone zone within the Tarkwa mining area using Digital Elevation Models (DEMs). A DEM-derived morphologic features including local slope and streamflow network were developed using ArcGIS. The flood susceptible map was generated by merging the slope map and the stream network maps. The results indicate that about 42.59% of the study area were highly prone to flooding. The areas susceptible to flooding were mainly made up of agricultural lands with high usage of chemical fertilizers for farming, which may leach into nearby rivers, streams, lakes and groundwater during flood occurrence. It is understood from this study that the mining areas within the Tarkwa Nsuaem municipality were less susceptible to flooding. The outcome of this study provides a simplified method of mapping and assessment of flood susceptible areas which can be used as a reference for flood risk management, prevention, and reduction of natural disasters in the study area.