International Journal of Disaster Risk Science

2024 Vol. 15, No. 1

column

ARTICLE
CORRECTION

Select articles

Display Method:

ARTICLE

Prioritizing Strategies for Building the Resilience of Public Health Systems to Disasters Across Multiple Communities and Countries

Benjamin J. Ryan, Mayumi Kako, Shelby Garner, Rok Fink, Ismail Tayfur, Jonathan Abrahams, Sanjaya Bhatia, Adriana Campelo, Matthew Fendt, Alicia Fontenot, Nahuel Arenas Garcia, Tim Hatch, Ryoma Kayano, LaShonda Malrey-Horne, Makiko MacDermot, Md Moshiur Rahman, Chaverle Noel, Shuhei Nomura, Jeremy P. Novak, Maria Opazo, Kendell Oliver, Luciana Peters, Sohel Rahman, Perihan Şimşek, Andrew Stricklin, Raymond Swienton, Bryan W. Brooks

2024, 15(1): 1-17. doi: 10.1007/s13753-024-00537-x

Abstract(35) PDF

Abstract:
The COVID-19 pandemic highlighted the urgent need to strengthen public health systems. In response, the United Nations Disaster Risk Reduction (UNDRR) Public Health System Resilience Scorecard (Scorecard) was applied in workshops across multiple countries. The aim of our research was to explore the workshop findings to develop priority strategies for strengthening public health system resilience. We conducted a workshop from 14 to 16 March 2023, at the UNDRR Global Education and Training Institute in Incheon, Republic of Korea. A sequential modified Delphi method was utilized to develop a set of prioritized resilience strategies. These were drawn from 70 strategies identified from 13 distinct workshops in eight countries. After two surveys, 23 strategies were finalized. Ten received ratings of “High” or “Very High” from 89% of participants. These related to the inclusion of public health risks in emergency plans, integrating multidisciplinary teams into public health, enabling local transport mechanisms, and improving the ability to manage an influx of patients. The Scorecard provides an adaptable framework to identify and prioritize strategies for strengthening public health system resilience. By leveraging this methodology, our study demonstrated how resilience strategies could inform disaster risk reduction funding, policies, and actions.

Applying Earth Observation Technologies to Economic Consequence Modeling: A Case Study of COVID-19 in Los Angeles County, California

Fynnwin Prager, Marina T. Mendoza, Charles K. Huyck, Adam Rose, Paul Amyx, Gregory Yetman, Kristy F. Tiampo

2024, 15(1): 18-31. doi: 10.1007/s13753-024-00543-z

Abstract(14) PDF

Abstract:
Earth observation (EO) technologies, such as very high-resolution optical satellite data available from Maxar, can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public. We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects. We applied our methodology to the COVID-19 pandemic experience in Los Angeles County, California as a case study. We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context. We found that despite limitations, EO data can increase sectoral and temporal resolution, which leads to significant differences from other data sources in terms of direct and total impact results. The findings from this analytical approach have important implications for economic consequence modeling of disasters, as well as providing useful information to policymakers and emergency managers, whose goal is to reduce disaster costs and to improve economic resilience.

A Framework to Assess Possible Sources of Stakeholder Exploitation: A Developing Country Case

Kamran Shafique, Syed Shams, Tapan Sarker

2024, 15(1): 32-44. doi: 10.1007/s13753-024-00538-w

Abstract(9) PDF

Abstract:
Solving complex post-disaster reconstruction challenges requires the altruistic involvement of heterogeneous stakeholder groups. However, small, more organized groups, such as government parastatals, private developers, and contractors often exploit large, unorganized groups, such as affected communities, leaving them more vulnerable to future disasters. Based on data collected from a case study in Pakistan, this study proposed a framework to assess, anticipate, and mitigate the exploitation of vulnerable stakeholders in post-disaster reconstruction projects. The framework draws on influential management theories and utilizes reciprocal relationships between stakeholder attributes (power, legitimacy, and urgency), participation, and exploitation. The study also argued for non-binary treatment of stakeholder attributes. The framework will allow practitioners to address issues around the exploitation of stakeholder interests in future post-disaster reconstruction projects.

A Systematic Analysis of Systems Approach and Flood Risk Management Research: Trends, Gaps, and Opportunities

Lum Sonita Awah, Johanes Amate Belle, Yong Sebastian Nyam, Israel Ropo Orimoloye

2024, 15(1): 45-57. doi: 10.1007/s13753-024-00544-y

Abstract(7) PDF

Abstract:
Flooding is a global threat, necessitating a comprehensive management approach. Due to the complexity of managing flood hazards and risks, researchers have advocated for holistic, comprehensive, and integrated approaches. This study, employing a systems thinking perspective, assessed global flood risk management research trends, gaps, and opportunities using 132 published documents in BibTeX format. A systematic review of downloaded documents from the Scopus and Web of Science databases revealed slow progress of approximately 11.61% annual growth in applying systems thinking and its concomitant approaches to understanding global flood risk management over the past two decades compared to other fields like water resource management and business management systems. A significant gap exists in the application of systems thinking methodologies to flood risk management research between developed and developing countries, particularly in Africa, highlighting the urgency of reoriented research and policy efforts. The application gaps of the study methodology are linked to challenges outlined in existing literature, such as issues related to technical expertise and resource constraints. This study advocates a shift from linear to holistic approaches in flood risk management, aligned with the Sendai Framework for Disaster Risk Reduction 2015–2023 and the Sustainable Development Goals. Collaboration among researchers, institutions, and countries is essential to address this global challenge effectively.

Factors Impacting Participatory Post-Disaster Relocation and Housing Reconstruction: The Case of Tsholotsho District, Zimbabwe

Nombulelo Kitsepile Ngulube, Hirokazu Tatano, Subhajyoti Samaddar

2024, 15(1): 58-72. doi: 10.1007/s13753-024-00536-y

Abstract(6) PDF

Abstract:
Relocation is not typically considered the best planning option for post-disaster reconstruction and rehabilitation, but it may be necessary if the site has suffered severe damage or is at imminent risk. There is a growing recognition that strong community participation is necessary in the post-disaster relocation decision-making process since relocation can have detrimental effects on a community’s livelihood, cultural system, and way of life, among others. However, the realization of this still needs to be improved. As of yet, few studies have examined a comprehensive account of meaningful community engagement in post-disaster relocation and reconstruction, particularly in developing countries. This study investigated what factors influenced local communities’ participation in post-disaster relocation and reconstruction works after the 2017 Cyclone Dineo flood disaster in the Tsholotsho District of Zimbabwe. Qualitative research methods such as face-to-face interviews, observations, and focus groups were used to collect qualitative data from a purposive sample of 25 community members and 6 stakeholders. This empirical investigation showed that despite the fact that the relocation project was conceived as a community-centered project, there was no meaningful community engagement, due to the absence of a participatory framework or planning guidelines for stakeholder engagement, as well as the lack of political willingness among government officials. The study concluded that the lack of community involvement led to local communities abandoning the reconstruction sites because relocation projects failed to accommodate the cultural beliefs, place attachments, and livelihood concerns of local communities. This study suggested that it is imperative to enhance the awareness of government officials and other stakeholders about the importance of community participation for the effective implementation of post-disaster relocation works. Meaningful community participation can also provide avenues for incorporating local needs and concerns, cultural beliefs, and alternative and sustainable livelihood restoration, which are essential for effective reconstruction after disasters. This research aimed to enrich the academic discourse by providing valuable insights into the intricacies of post-disaster recovery initiatives in the country.

Impact of Media Information on Social Response in Disasters: A Case Study of the Freezing-Rain and Snowstorm Disasters in Southern China in 2008

Jia He, Wenjing Duan, Yuxuan Zhou, Yun Su

2024, 15(1): 73-87. doi: 10.1007/s13753-024-00539-9

Abstract(4) PDF

Abstract:
Disaster information content is an objective mapping of disaster situations, social response, and public opinions. Social response to emergency is an important mechanism for implementing and guaranteeing emergency management of major natural hazard-related disasters. Understanding how disaster information content affects social response to emergencies is helpful for managing risk communication and efficient disaster response. Based on the 2008 freezing-rain and snowstorm disasters in southern China, this study used Python to extract 7,857 case-related media reports and applied natural language processing for text analysis. It used three typical cases to identify and analyze disaster media report content and the relationship between these reports and the social response to the emergency. Eight categories of disaster response—such as prewarning and forecasting, announcements by the authorities, and social mobilization—appeared in the disaster information in the media, along with disaster impact information, that is, real-time disaster status. Disaster response information and an appropriate amount of disaster impact information played important roles in prewarning, disaster relief, public opinion guidance, and social stability maintenance and can serve important functions in communicating with all stakeholders of emergency management, assisting or influencing emergency departments or individuals in decision making, and eliminating “information islands.” Empathy caused the general public to become “disaster responders” through receiving information. Rumors and an excess of negative information may have a perverse amplification effect on public opinion and increase the unpredictability of the disaster situation and the risk of social crisis.

Assessing Multidimensional Vulnerability of Rural Areas to Flooding: An Index-Based Approach

Samavia Rasool, Irfan Ahmad Rana, Hassam Bin Waseem

2024, 15(1): 88-106. doi: 10.1007/s13753-024-00547-9

Abstract(9) PDF

Abstract:
Vulnerability assessment is essential for understanding and launching effective flood risk reduction strategies. This study aimed to examine the vulnerability of flood-prone rural communities in southern Punjab, Pakistan to external shocks. The concept of vulnerability encompasses a range of dimensions, including physical, social, institutional, environmental, economic, and attitudinal. Using a composite index method, indices were developed for each dimension and combined to create a multidimensional measure of vulnerability. A sample of 365 communities was selected using the Yamane sampling technique, and data were collected through a questionnaire containing 65 indicators across all dimensions. Descriptive statistics and ANOVA tests were used to analyze the data. The results show that communities near the Chenab River had higher attitudinal and institutional vulnerability compared to other communities. High attitudinal vulnerabilities were associated with poorly perceived flood risks and low preparedness measures, whereas institutional vulnerabilities were driven by conventional flood protection strategies, lack of institutional trust, and lack of flood risk awareness. This research provides insights into the various components of vulnerability in flood-prone rural communities in Pakistan and demonstrates a useful methodology that can be applied to other disasters at different spatial scales.

Design of Resilient Sensor Networks Balancing Resilience and Efficiency

Sergey N. Vecherin, Kiril D. Ratmanski, Luke Hogewood, Igor Linkov

2024, 15(1): 107-115. doi: 10.1007/s13753-024-00546-w

Abstract(14) PDF

Abstract:
In recent years, the notion of resilience has been developed and applied in many technical areas, becoming exceptionally pertinent to disaster risk science. During a disaster situation, accurate sensing information is the key to efficient recovery efforts. In general, resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality, but resilient design may require redundancy and could increase costs. In this article, we describe a method based on binary linear programming for sensor network design balancing efficiency with resilience. The application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both two- and three-dimensional spaces. The method provides optimal sensor placement, taking into account critical functionality and a desired level of resilience and considering sensor type and availability. The problem formulation, resilience requirements, and application of the optimization algorithm are described in detail. Analysis of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent placement. Both tasks are successfully solved by the described method, which can be applied to strengthen the resilience of sensor networks by design. The proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.

Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

Hengxu Jin, Yu Zhao, Pengcheng Lu, Shuliang Zhang, Yiwen Chen, Shanghua Zheng, Zhizhou Zhu

2024, 15(1): 116-133. doi: 10.1007/s13753-024-00540-2

Abstract(10) PDF

Abstract:
This study presents a novel method for optimizing parameters in urban flood models, aiming to address the tedious and complex issues associated with parameter optimization. First, a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model. Next, a principle for dividing urban hydrological response units was introduced, incorporating surface attribute features. The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model, and an artificial neural network (ANN) was employed to identify the sensitive parameters. Finally, a genetic algorithm (GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model. The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient (NSE) of 0.81. Compared to the ANN-GA and K-means-deep neural networks (DNN) methods, the proposed method better characterizes the runoff generation and flow processes. This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods

Yuran Sun, Shih-Kai Huang, Xilei Zhao

2024, 15(1): 134-148. doi: 10.1007/s13753-024-00541-1

Abstract(9) PDF

Abstract:
Facing the escalating effects of climate change, it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management. Current studies in this area often have relied on psychology-driven linear models, which frequently exhibited limitations in practice. The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors, compared to existing models that mainly rely on psychological factors. An enhanced logistic regression model (that is, an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions (that is, univariate and bivariate threshold effects). Specifically, nonlinearity and interaction detection were enabled by low-depth decision trees, which offer transparent model structure and robustness. A survey dataset collected in the aftermath of Hurricanes Katrina and Rita, two of the most intense tropical storms of the last two decades, was employed to test the new methodology. The findings show that, when predicting the households’ evacuation decisions, the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability. This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.

Economic Risk Assessment of Future Debris Flows by Machine Learning Method

Chenchen Qiu, Lijun Su, Alessandro Pasuto, Giulia Bossi, Xueyu Geng

2024, 15(1): 149-164. doi: 10.1007/s13753-024-00545-x

Abstract(12) PDF

Abstract:
A reliable economic risk map is critical for effective debris-flow mitigation. However, the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application. To estimate the economic risks caused by future debris flows, a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map. We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year. The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact, temporal probability, and annual susceptibility. We employed a hybrid machine learning model—certainty factor-genetic algorithm-support vector classification—to calculate susceptibilities. Simultaneously, a Poisson model was applied for temporal probabilities, while the determination of annual probability of spatial impact relied on statistical results. Additionally, four major elements at risk were selected for the generation of an economic loss map: roads, vegetation-covered land, residential buildings, and farmland. The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values. Therefore, we proposed a physical vulnerability matrix for residential buildings, factoring in impact pressure on buildings and their horizontal distance and vertical distance to debris-flow channels. In this context, an ensemble model (XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings. The results show that residential buildings occupy 76.7% of the total economic risk, while road-covered areas contribute approximately 6.85%. Vegetation-covered land and farmland collectively represent 16.45% of the entire risk. These findings can provide a scientific support for the effective mitigation of future debris flows.

A Physics-Based Seismic Risk Assessment of the Qujiang Fault: From Dynamic Rupture to Disaster Estimation

Yilong Li, Zijia Wang, Zhenguo Zhang, Yuhao Gu, Houyun Yu

2024, 15(1): 165-177. doi: 10.1007/s13753-024-00542-0

Abstract(10) PDF

Abstract:
This study achieved the construction of earthquake disaster scenarios based on physics-based methods—from fault dynamic rupture to seismic wave propagation—and then population and economic loss estimations. The physics-based dynamic rupture and strong ground motion simulations can fully consider the three-dimensional complexity of physical parameters such as fault geometry, stress field, rock properties, and terrain. Quantitative analysis of multiple seismic disaster scenarios along the Qujiang Fault in western Yunnan Province in southwestern China based on different nucleation locations was achieved. The results indicate that the northwestern segment of the Qujiang Fault is expected to experience significantly higher levels of damage compared to the southeastern segment. Additionally, there are significant variations in human losses, even though the economic losses are similar across different scenarios. Dali Bai Autonomous Prefecture, Chuxiong Yi Autonomous Prefecture, Yuxi City, Honghe Hani and Yi Autonomous Prefecture, and Wenshan Zhuang and Miao Autonomous Prefecture were identified as at medium to high seismic risks, with Yuxi and Honghe being particularly vulnerable. Implementing targeted earthquake prevention measures in Yuxi and Honghe will significantly mitigate the potential risks posed by the Qujiang Fault. Notably, although the fault is within Yuxi, Honghe is likely to suffer the most severe damage. These findings emphasize the importance of considering rupture directivity and its influence on ground motion distribution when assessing seismic risk.

CORRECTION

Correction to: Advancing the Disaster and Development Paradigm

Andrew E. Collins

2024, 15(1): 178-178. doi: 10.1007/s13753-024-00534-0

Abstract(12) PDF

Abstract:

Correction to: A Two-Stage Evolutionary Game Model for Collaborative Emergency Management Between Local Governments and Enterprises

Yanqing Wang, Hong Chen, Xiao Gu

2024, 15(1): 179-179. doi: 10.1007/s13753-024-00535-z

Abstract(12) PDF

Abstract: