GIS-aided avalanche warning in Norway

Jaedicke, Christian; Syre, Egil; Sverdrup-Thygeson, Kjetil

doi:10.1016/j.cageo.2014.01.004

Cited by 12 publications

(9 citation statements)

References 5 publications

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“…The Geographic Information Systems (GIS) have been identified as a powerful tool for snow avalanche terrain recognition, suitable for mountain risk and disaster management, with high capacity to enhance terrain visualization models (Jaedicke et al, 2014;Bourova et al, 2016). GIS has long enabled the web-based visualizing terrain software packages for snow avalanche hazard assessment (Stoffel et al, 2001;Haid et al, 2008;Lan et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Snow avalanche hazard prediction using machine learning methods

Choubin

Borji

Mosavi

et al. 2019

Journal of Hydrology

123

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Snow avalanches are among the most destructive natural hazards threatening human life, ecosystems, built structures, and landscapes in mountainous regions. The complexity of snow avalanche modelling has been discussed in many studies, but its modelling is not well-documented. Snow avalanche modeling in this study was done using three main categories of data, including avalanche occurrence locations, meteorological factors, and terrain characteristics. Two machine learning models, namely support vector machine (SVM) and multivariate discriminant analysis (MDA), were employed. A ratio of 70 to 30 of data was considered for calibrating and validating the models. Results indicated that both models had an excellent performance in snow avalanche modeling (area under curve, AUC > 90), although hits and misses analysis demonstrated the superior performance of MDA. Sensitivity analysis indicated that the topographic position index, slope, precipitation, and topographic wetness index were the most effective variables for modeling. A snow avalanche map indicated that the high snow avalanche hazard zone was mostly near the streams and was matched with hillsides around the water pathways. Findings of study can be helpful for land use planning, to control snow avalanche paths, and to prevent the probable hazards induced by it, and it can be a good reference for future studies on modeling snow avalanche hazards.

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Section: Introductionmentioning

confidence: 99%

Snow avalanche hazard prediction using machine learning methods

Choubin

Borji

Mosavi

et al. 2019

Journal of Hydrology

123

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“…As an example of application, Jaedicke et al (2014) uses a GIS (Geographic Information Systems) solution to warn avalanches in Norway. Maps are used in study showing areas susceptible to occurrence of avalanches providing an overview on the overall situation.…”

Section: Risk Visualizationmentioning

confidence: 99%

Basic Concepts on Risk Analysis, Reliability and Maintenance

Almeida

Cavalcante

Alencar

et al. 2015

International Series in Operations Research &Amp; Management Science

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Abstract:Man's level of dependence on equipment is increasing. This degree of dependence requires high levels of availability, which has been changing the impact that disruption of these systems causes. For many systems, an interruption has consequences that go beyond the dimension of financial loss, thus justifying a multidimensional consequence approach by using multicriteria (MCDM/A) models. Thus, understanding the relationships between and among risk, reliability and maintenance (RRM) is essential in order to offer more comprehensive solutions to the various problems often treated in isolation from each other, and which are the most important problems of the competitive market. This chapter discusses fundamental topics about RRM, including tools for risk analysis and hazard identification, concepts of reliability, maintenance techniques such as RCM and TPM and eliciting expert's knowledge. These topics are presented in order to provide a basis for structuring different MCDM/A problems that are addressed in several chapters. Some fundamental aspects could be used as input to decision models in different forms such as attributes, objectives, criteria, and problem context. Basic Concepts on Risk AnalysisThere are many concepts on risk found in the literature and also different perceptions to it. However, if a decision is being made and risk is involved, then, the risk concept should combine consequences and probabilities, incorporating the DM's preferences over that, as seen in Chap. 2.Actually, a 'decision process' with no DM's preference has no decision being made, as discussed at the end of Chap. 1. Instead of that, that process either: a) has some preference structure incorporated within the model, at random; b) is just arbitrary following a previous decision of someone else.Even so, in most of real cases, the consequences are multidimensional, and therefore, require an MCDM/A approach for building a decision model. The following topics are mainly based on the basic RRM literature and do not incorporate the idea of decision support, as given in Chap. 2. That is, DM's preferences are not necessarily considered in the model.

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“…They are used to support simultaneous access for a wide range of users with different roles. Such new web-based tools with computer assisted workflow have been recently implemented to support the forecasters in producing avalanche bulletins in Switzerland (Ruesch et al, 2013) and in Norway (Jaedicke et al, 2014). In Austria and Italy, web-based platforms have been developed to support the management of local avalanche hazard (Segor et al, 2014).…”

Section: E Bourova Et Al: a Web-based Information System For Snow Amentioning

confidence: 99%

A new web-based system to improve the monitoring of snow avalanche hazard in France

Bourova¹,

Maldonado²,

Leroy³

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. Snow avalanche data in the French Alps and Pyrenees have been recorded for more than 100 years in several databases. The increasing amount of observed data required a more integrative and automated service. Here we report the comprehensive web-based Snow Avalanche Information System newly developed to this end for three important data sets: an avalanche chronicle (Enquête Permanente sur les Avalanches, EPA), an avalanche map (Carte de Localisation des Phénomènes d'Avalanche, CLPA) and a compilation of hazard and vulnerability data recorded on selected paths endangering human settlements (Sites Habités Sensibles aux Avalanches, SSA). These data sets are now integrated into a common database, enabling full interoperability between all different types of snow avalanche records: digitized geographic data, avalanche descriptive parameters, eyewitness reports, photographs, hazard and risk levels, etc. The new information system is implemented through modular components using Java-based web technologies with Spring and Hibernate frameworks. It automates the manual data entry and improves the process of information collection and sharing, enhancing user experience and data quality, and offering new outlooks to explore and exploit the huge amount of snow avalanche data available for fundamental research and more applied risk assessment.

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GIS-aided avalanche warning in Norway

Cited by 12 publications

References 5 publications

Snow avalanche hazard prediction using machine learning methods

Snow avalanche hazard prediction using machine learning methods

Basic Concepts on Risk Analysis, Reliability and Maintenance

A new web-based system to improve the monitoring of snow avalanche hazard in France

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