Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs

Guikema, Seth D.

doi:10.1111/risa.13476

Cited by 41 publications

(29 citation statements)

References 26 publications

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“…This is driven by developments in information and communication technology, including sensor and storage technology, coupled with developments in artificial intelligence (AI) and statistics, including data mining, machine learning, and statistical learning theory (e.g., Hastie, Tibshirani, & Friedman, 2017;Vapnik, 2013). Guikema (2020) discusses the use of AI methods in the context of natural hazards risk analysis. He highlights what characterizes settings where AI methods have shown success and points out challenges with using such methods in this context.…”

Section: Current Developments and Trendsmentioning

confidence: 99%

Foundational Challenges for Advancing the Field and Discipline of Risk Analysis

Aven

Flage

2020

Risk Analysis

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Risk analysis as a field and discipline is about concepts, principles, approaches, methods, and models for understanding, assessing, communicating, managing, and governing risk. The foundation of this field and discipline has been subject to continuous discussion since its origin some 40 years ago with the establishment of the Society for Risk Analysis and the Risk Analysis journal. This article provides a perspective on critical foundational challenges that this field and discipline faces today, for risk analysis to develop and have societal impact. Topics discussed include fundamental questions important for defining the risk field, discipline, and science; the multidisciplinary and interdisciplinary features of risk analysis; the interactions and dependencies with other sciences; terminology and fundamental principles; and current developments and trends, such as the use of artificial intelligence.

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Section: Current Developments and Trendsmentioning

confidence: 99%

Foundational Challenges for Advancing the Field and Discipline of Risk Analysis

Aven

Flage

2020

Risk Analysis

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“…Using operational datasets to build predictive models will continue to face issues with messy and inconsistent patterns that could perpetuate errors in human assessments (Guikema, 2020). The decision trees presented in this study only showed the most significant variables by restricting the p value to 0.0001 to keep the diagrams tidy and easy to interpret.…”

Section: Applications and Limitations Of Decision Support Toolsmentioning

confidence: 96%

“…1). The region is characterized by a transitional snow climate that favours heavy snowfall and the formation of multiple persistent weak layers each season (Haegeli and McClung, 2007;Shandro and Haegeli, 2018). The park is located at approximately 51.3 • N, 117.5 • W. Elevations range from 805 to 3377 m, with the typical treeline vegetation band around Weather and snowpack data were generated with numerical weather prediction and physical snow cover models for each elevation band.…”

Section: Study Area and Periodmentioning

confidence: 99%

Examining the operational use of avalanche problems with decision trees and model-generated weather and snowpack variables

Horton

Towell

Haegeli

2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Avalanche problems are used in avalanche forecasting to describe snowpack, weather, and terrain factors that require distinct risk management techniques. Although they have become an effective tool for assessing and communicating avalanche hazard, their definitions leave room for interpretation and inconsistencies. This study uses conditional inference trees to explore the application of avalanche problems over eight winters in Glacier National Park, Canada. The influences of weather and snowpack variables on each avalanche problem type were explored by analysing a continuous set of weather and snowpack variables produced with a numerical weather prediction model and a physical snow cover model. The decision trees suggest forecasters' assessments are based on not only a physical analysis of weather and snowpack conditions but also contextual information about the time of season, the location, and interactions with other avalanche problems. The decision trees showed clearer patterns when new avalanche problems were added to hazard assessments compared to when problems were removed. Despite discrepancies between modelled variables and field observations, the model-generated variables produced intuitive explanations for conditions influencing most avalanche problem types. For example, snowfall in the past 72 h was the most significant variable for storm slab avalanche problems, skier penetration depth was the most significant variable for dry loose avalanche problems, and slab density was the most significant variable for persistent-slab avalanche problems. The explanations for wind slab and cornice avalanche problems were less intuitive, suggesting potential inconsistencies in their application as well as shortcomings of the model-generated data. The decision trees illustrate how forecasters apply avalanche problems and can inform discussions about improved operational practices and the development of data-driven decision aids.

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“…This data will facilitate the prediction of the emergency scenario and understand human behaviour by assessing underline patterns of media users. Social sensing has advantages over field surveys and interviews, as it gathers data from the public, not directly from the disaster responders, provides real-time data of the disaster scenario and facilitate in making a timely decision for mitigating the disaster and informing the public about evacuation plans [75].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

et al. 2021

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Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.

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Artificial Intelligence for Natural Hazards Risk Analysis: Potential, Challenges, and Research Needs

Cited by 41 publications

References 26 publications

Foundational Challenges for Advancing the Field and Discipline of Risk Analysis

Foundational Challenges for Advancing the Field and Discipline of Risk Analysis

Examining the operational use of avalanche problems with decision trees and model-generated weather and snowpack variables

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

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