International Journal of Disaster Risk Science

2020 Vol. 11, No. 6

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Data-Driven Approaches to Integrated Disaster Risk Management

Vincent Lemiale, Mahesh Prakash, Ana Maria Cruz

2020, 11(6): 693-695. doi: 10.1007/s13753-020-00324-4

Abstract(324) PDF

Abstract:

Variability in Regional Ecological Vulnerability: A Case Study of Sichuan Province, China

Yimeng Liu, Saini Yang, Chuanliang Han, Wei Ni, Yuyao Zhu

2020, 11(6): 696-708. doi: 10.1007/s13753-020-00295-6

Abstract(386) PDF

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Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide. Using land use, socioeconomic, and natural hazards data, we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005, 2010, and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations. We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index (EVI), and constructed the EVI using a principal component analysis-based entropy method. A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability. The results show that: (1) for each of the study years, prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan, whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability; (2) Sichuan's ecological vulnerability increased significantly (p = 0.011) during 2005–2010; (3) anthropogenic activities were the main factors driving variations in ecological vulnerability. These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.

Seismic Policy, Operations, and Research Uses for a Building Inventory in an Earthquake-Prone City

Ken Elwood, Olga Filippova, Ilan Noy, Jacob Pastor Paz

2020, 11(6): 709-718. doi: 10.1007/s13753-020-00313-7

Abstract(552) PDF

Abstract:
After the 2016 New Zealand Kaikoura Earthquake, the absence of information about the state of buildings in Wellington proved to be a source of significant policy uncertainty. Authorities did not know what damages to expect and therefore how to react, and policies needed to be formulated without a clear quantification of the risks. Moreover, without detailed knowledge of the existing buildings, it was difficult to assess what the available legal and regulatory tools can achieve and choose among them. We describe the creation of a building inventory database for Wellington initiated by the authors. This database aims to assist the generation of research on the risks, impacts, and viable solutions for reducing future seismic risk in Wellington's central business district (CBD). The database includes structural, economic, and market information on virtually every significant building in the CBD. Its primary purposes are: to collate and provide the best available information about the expected seismic performance of the existing building stock; to assess the impact of possible multiple building failures due to a seismic event; to describe a viable cost-effective path for seismic retrofitting; and to inform the design of a regulatory structure that can facilitate this resilience-building agenda.

Disaster Risk Management Through the DesignSafe Cyberinfrastructure

Jean-Paul Pinelli, Maria Esteva, Ellen M. Rathje, David Roueche, Scott J. Brandenberg, Gilberto Mosqueda, Jamie Padgett, Frederick Haan

2020, 11(6): 719-734. doi: 10.1007/s13753-020-00320-8

Abstract(373) PDF

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DesignSafe addresses the challenges of supporting integrative data-driven research in natural hazards engineering. It is an end-to-end data management, communications, and analysis platform where users collect, generate, analyze, curate, and publish large data sets from a variety of sources, including experiments, simulations, field research, and post-disaster reconnaissance. DesignSafe achieves key objectives through: (1) integration with high performance and cloud-computing resources to support the computational needs of the regional risk assessment community; (2) the possibility to curate and publish diverse data structures emphasizing relationships and understandability; and (3) facilitation of real time communications during natural hazards events and disasters for data and information sharing. The resultant services and tools shorten data cycles for resiliency evaluation, risk modeling validation, and forensic studies. This article illustrates salient features of the cyberinfrastructure. It summarizes its design principles, architecture, and functionalities. The focus is on case studies to show the impact of DesignSafe on the disaster risk community. The Next Generation Liquefaction project collects and standardizes case histories of earthquake-induced soil liquefaction into a relational database—DesignSafe—to permit users to interact with the data. Researchers can correlate in DesignSafe building dynamic characteristics based on data from building sensors, with observed damage based on ground motion measurements. Reconnaissance groups upload, curate, and publish wind, seismic, and coastal damage data they gather during field reconnaissance missions, so these datasets are available shortly after a disaster. As a part of the education and community outreach efforts of DesignSafe, training materials and collaboration space are also offered to the disaster risk management community.

Extracting Natech Reports from Large Databases: Development of a Semi-Intelligent Natech Identification Framework

Xiaolong Luo, Ana Maria Cruz, Dimitrios Tzioutzios

2020, 11(6): 735-750. doi: 10.1007/s13753-020-00314-6

Abstract(292) PDF

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Natural hazard-triggered technological accidents (Natechs) refer to accidents involving releases of hazardous materials (hazmat) triggered by natural hazards. Huge economic losses, as well as human health and environmental problems are caused by Natechs. In this regard, learning from previous Natechs is critical for risk management. However, due to data scarcity and high uncertainty concerning such hazards, it becomes a serious challenge for risk managers to detect Natechs from large databases, such as the National Response Center (NRC) database. As the largest database of hazmat release incidents, the NRC database receives hazmat release reports from citizens in the United States. However, callers often have incomplete details about the incidents they are reporting. This results in many records having incomplete information. Consequently, it is quite difficult to identify and extract Natechs accurately and efficiently. In this study, we introduce machine learning theory into the Natech retrieving research, and a Semi-Intelligent Natech Identification Framework (SINIF) is proposed in order to solve the problem. We tested the suitability of two supervised machine learning algorithms, namely the Long ShortTerm Memory (LSTM) and the Convolutional Neural Network (CNN), and selected the former for the development of the SINIF. According to the results, the SINIF is efficient (a total number of 826,078 records were analyzed) and accurate (the accuracy is over 0.90), while 32,841 Natech reports between 1990 and 2017 were extracted from the NRC database. Furthermore, the majority of those Natech reports (97.85%) were related to meteorological phenomena, with hurricanes (24.41%), heavy rains (19.27%), and storms (18.29%) as the main causes of these reported Natechs. Overall, this study suggests that risk managers can benefit immensely from SINIF in analyzing Natech data from large databases efficiently.

Development and Social Implementation of Smartphone App Nige-Tore for Improving Tsunami Evacuation Drills: Synergistic Effects Between Commitment and Contingency

Katsuya Yamori, Takashi Sugiyama

2020, 11(6): 751-761. doi: 10.1007/s13753-020-00319-1

Abstract(297) PDF

Abstract:
This research explored how we can improve tsunami evacuation behavior, which has been a major social issue since the 2011 Great East Japan Earthquake and Tsunami. We introduce Nige-Tore, a smartphone app for supporting tsunami evacuation drills, which was developed within an interdisciplinary research framework. Nige-Tore serves as an effective interface tool that successfully visualizes the dynamic interactions between human actions (evacuation behavior) and natural phenomena (tsunami behavior). Drill participants can check, on their smartphone, the estimated inundation area of the approaching tsunami, along with their own current evacuation trajectory. The results of real-world trials using NigeTore show that the app is more powerful than conventional devices and methods that have been used in tsunami evacuation training, such as hazard maps and traditional drills that do not make use of any apps, because Nige-Tore provides an interface that enables commitment and contingency thinking—which at first glance appear to represent different orientations—to not only coexist but to synergize. “Commitment” (devotion or involvement) refers to the act of immersing oneself in and viewing as absolute one particular scenario or its potential to be actualized, given conditions in which infinite scenarios may be actualized, depending on the interactions between human systems and natural systems. “Contingency” thinking (an accidental or incidental state) refers to the act of relativizing and separating oneself from any particular scenario or its potential to be actualized, given the same conditions. The synergistic effect of “commitment” and “contingency” thinking also expands people's capacity to cope with unexpected and unforeseen events.

Synergistic Integration of Detailed Meteorological and Community Information for Evacuation from Weather-Related Disasters: Proposal of a “Disaster Response Switch”

Kensuke Takenouchi, Katsuya Yamori

2020, 11(6): 762-775. doi: 10.1007/s13753-020-00317-3

Abstract(496) PDF

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Meteorological information used for disaster prevention has developed rapidly in terms of both type and specificity. The latest forecasting models can predict weather with very high resolutions that can characterize disaster risk at the local level. However, this development can lead to an overdependency on the information and a wait-and-see attitude by the public. At the same time, residents share and use various types of information for disaster response, such as local conditions, in addition to official disaster information. Our research in Japan verified the practicality and efficiency of synergistically integrating these types of information by examining actual evacuation cases. The current numerical forecasting models sufficiently identify locality from the viewpoint of various administrative scales such as prefectures, municipalities, and school districts, but the improvements to these models have failed to improve residents' judgment in successful evacuation cases. We therefore analyzed the relationship between meteorological information and residents' disaster response and confirmed that they were strongly correlated and were contributing factors in preventing disasters. We revealed differences between a community's disaster prevention culture and the disaster information provided. This led us to propose a new concept in community disaster prevention that we call the “disaster response switch,” which can serve as a data-driven risk management tool for communities when used in combination with advanced meteorological disaster information.

Naïve Bayes Classifier for Debris Flow Disaster Mitigation in Mount Merapi Volcanic Rivers, Indonesia, Using X-band Polarimetric Radar

Ratih Indri Hapsari, Bima Ahida Indaka Sugna, Dandung Novianto, Rosa Andrie Asmara, Satoru Oishi

2020, 11(6): 776-789. doi: 10.1007/s13753-020-00321-7

Abstract(482) PDF

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Debris flow triggered by rainfall that accompanies a volcanic eruption is a serious secondary impact of a volcanic disaster. The probability of debris flow events can be estimated based on the prior information of rainfall from historical and geomorphological data that are presumed to relate to debris flow occurrence. In this study, a debris flow disaster warning system was developed by applying the Naïve Bayes Classifier (NBC). The spatial likelihood of the hazard is evaluated at a small subbasin scale by including high-resolution rainfall measurements from X-band polarimetric weather radar, a topographic factor, and soil type as predictors. The study was conducted in the Gendol River Basin of Mount Merapi, one of the most active volcanoes in Indonesia. Rainfall and debris flow occurrence data were collected for the upper Gendol River from October 2016 to February 2018 and divided into calibration and validation datasets. The NBC was used to estimate the status of debris flow incidences displayed in the susceptibility map that is based on the posterior probability from the predictors. The system verification was performed by quantitative dichotomous quality indices along with a contingency table. Using the validation datasets, the advantage of the NBC for estimating debris flow occurrence is confirmed. This work contributes to existing knowledge on estimating debris flow susceptibility through the data mining approach. Despite the existence of predictive uncertainty, the presented system could contribute to the improvement of debris flow countermeasures in volcanic regions.

Uncertainty Reduction Through Data Management in the Development, Validation, Calibration, and Operation of a Hurricane Vulnerability Model

Jean-Paul Pinelli, Josemar Da Cruz, Kurtis Gurley, Andres Santiago Paleo-Torres, Mohammad Baradaranshoraka, Steven Cocke, Dongwook Shin

2020, 11(6): 790-806. doi: 10.1007/s13753-020-00316-4

Abstract(605) PDF

Abstract:
Catastrophe models estimate risk at the intersection of hazard, exposure, and vulnerability. Each of these areas requires diverse sources of data, which are very often incomplete, inconsistent, or missing altogether. The poor quality of the data is a source of epistemic uncertainty, which affects the vulnerability models as well as the output of the catastrophe models. This article identifies the different sources of epistemic uncertainty in the data, and elaborates on strategies to reduce this uncertainty, in particular through identification, augmentation, and integration of the different types of data. The challenges are illustrated through the Florida Public Hurricane Loss Model (FPHLM), which estimates insured losses on residential buildings caused by hurricane events in Florida. To define the input exposure, and for model development, calibration, and validation purposes, the FPHLM teams accessed three main sources of data: county tax appraiser databases, National Flood Insurance Protection (NFIP) portfolios, and wind insurance portfolios. The data from these different sources were reformatted and processed, and the insurance databases were separately cross-referenced at the county level with tax appraiser databases. The FPHLM hazard teams assigned estimates of natural hazard intensity measure to each insurance claim. These efforts produced an integrated and more complete set of building descriptors for each policy in the NFIP and wind portfolios. The article describes the impact of these uncertainty reductions on the development and validation of the vulnerability models, and suggests avenues for data improvement. Lessons learned should be of interest to professionals involved in disaster risk assessment and management.

Characterizing Uncertainty in City-Wide Disaster Recovery through Geospatial Multi-Lifeline Restoration Modeling of Earthquake Impact in the District of North Vancouver

Andrew Deelstra, David Bristow

2020, 11(6): 807-820. doi: 10.1007/s13753-020-00323-5

Abstract(578) PDF

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Restoring lifeline services to an urban neighborhood impacted by a large disaster is critical to the recovery of the city as a whole. Since cities are comprised of many dependent lifeline systems, the pattern of the restoration of each lifeline system can have an impact on one or more others. Due to the often uncertain and complex interactions between dense lifeline systems and their individual operations at the urban scale, it is typically unclear how different patterns of restoration will impact the overall recovery of lifeline system functioning. A difficulty in addressing this problem is the siloed nature of the knowledge and operations of different types of lifelines. Here, a city-wide, multi-lifeline restoration model and simulation are provided to address this issue. The approach uses the Graph Model for Operational Resilience, a data-driven discrete event simulator that can model the spatial and functional cascade of hazard effects and the pattern of restoration over time. A novel case study model of the District of North Vancouver is constructed and simulated for a reference magnitude 7.3 earthquake. The model comprises municipal water and wastewater, power distribution, and transport systems. The model includes 1725 entities from within these sectors, connected through 6456 dependency relationships. Simulation of the model shows that water distribution and wastewater treatment systems recover more quickly and with less uncertainty than electric power and road networks. Understanding this uncertainty will provide the opportunity to improve data collection, modeling, and collaboration with stakeholders in the future.