Review of literature on decision support systems for natural hazard risk reduction: Current status and future research directions

Newman, J. C.; Maier, Holger R.; Riddell, Graeme; Zecchin, Aaron C.; Daniell, James; Schaefer, Andreas; Delden, Hedwig van; Khazai, Bijan; O'Flaherty, Michael John; Newland, Charles Peter

doi:10.1016/j.envsoft.2017.06.042

Cited by 105 publications

(64 citation statements)

References 202 publications

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“…Natural hazards, such as floods, wildfires, earthquakes, tsunamis, and hurricanes, are causing significant losses worldwide, both in terms of lives lost and economic costs (McMullen & Lytle, ; Newman et al, ; Wen et al, ; Westra et al, ). Data are needed to support all stages of natural hazard management, including preparedness and response (Anson et al, ).…”

Section: Review Methodologymentioning

confidence: 99%

“…Examples of such data include real‐time information on the location, extent, and changes in hazards, as well as information on their impacts (e.g., losses, missing persons), to assist with the development of situational awareness (Akhgar et al, ; Stern, ), assess damage and suffering (Akhgar et al, ), and justify actions prior, during, and after disasters (Stern, ). In addition, data, and models developed with such data, are needed to identify risks and the impact of different risk reduction strategies (Anson et al, ; Newman et al, ).…”

Section: Review Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

110

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Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

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Section: Review Methodologymentioning

confidence: 99%

Section: Review Methodologymentioning

confidence: 99%

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

110

View full text Add to dashboard Cite

show abstract

“…However, people can be unwilling to purchase insurance against floods if they are not rewarded by reduced premiums when they make the effort to lower their vulnerability. Moreover, insurance can give biased information by encouraging building in flood-prone areas when premiums are not appropriately calculated according to risk (Newman et al, 2017). In terms of which type of PLFRA measures people implement (Section 2), personal competences and kind of PLFRA measures can stimulate local adaptation behavior (Thieken, Kreibich, Müller, & Merz, 2007;Van Valkengoed & Steg, 2019).…”

Section: Explanatory Variables Used In Preparedness Theoriesmentioning

confidence: 99%

Implementation of property‐level flood risk adaptation (PLFRA) measures: Choices and decisions

et al. 2019

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Hydrometeorological events are highly costly and have strong impacts on the human‐environment system. Effective response requires effective risk management concepts and strategies at individual and watershed level to increase community resilience. Focusing on flood risk and the information associated with it, individual risk behavior in the shape of implementing property‐level flood risk adaptation (PLFRA) measures is often overlooked. For this research, a comprehensive overview of possible PLFRA measures for homeowners in flood risk areas was made, as well as the possible costs and technical feasibility for new and existing buildings. To complement this, insights into risk mitigation behavior are essential due to the ongoing shift to risk‐based and individualized flood risk management, which require a contribution from flood‐prone households to risk reduction. Results show that PLFRA measures differentiate in their effectiveness, cost‐efficiency and technical feasibility, and full protection can never be guaranteed. Considering risk mitigation behavior, literature generally distinguishes between situational factors (such as communication and economic subsidies) and personal factors (such as personal and psychological components influencing individual behavior). This article is categorized under: Engineering Water > Planning Water Science of Water > Water Extremes

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“…In the domain of disaster risk reduction, Newman et al [1] states that the development and use of decision support systems is increasingly important, mainly because natural hazards are having a significant impact on communities and economies; losses due to natural disasters are expected to increase into the future; risk reduction is broadly recognized as being more effective than response and recovery and risk reduction and residual risk affect communities and the natural environment in multiple ways, with complexity and uncertainty in causal processes driving hazard impacts.…”

Section: Introductionmentioning

confidence: 99%

Integrating Risk Assessment into Spatial Planning: RiskOTe Decision Support System

Mileu

Queirós

2018

IJGI

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Abstract:The way in which risk maps are used in decision support processes for spatial planning at local scale is critical to helping decision makers in the definition of a prevention strategy to minimize risks. This paper presents a spatial decision support system that is developed to assist spatial planning by integrating the risk management component at municipal level. For the development of the RiskOTe tool, a semi-quantitative risk assessment model was used that assumes risk management to be comprehensive with respect to the type of hazard, vulnerability and risk minimization measures. In this paper, the components for the development of the spatial decision support system are identified, described and implemented using the municipality of Oeiras, in Portugal, as case study. The use of the system allowed the generation of multiple scenarios and outcomes. The results demonstrate that decision-making on the transformation of land uses by integrating risk analysis can be supported on a solid basis of information obtained from a spatial decision support system.

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Review of literature on decision support systems for natural hazard risk reduction: Current status and future research directions

Cited by 105 publications

References 202 publications

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Implementation of property‐level flood risk adaptation (PLFRA) measures: Choices and decisions

Integrating Risk Assessment into Spatial Planning: RiskOTe Decision Support System

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