https://www.zora.uzh.ch/id/eprint/177640/1/1-s2.0-S0165232X19302071-main.pdf

Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future

pdf/snow-avalanche-hazard-prediction-using-machine-learning-29i8wjebul.pdf

Snow avalanche hazard prediction using machine learning methods

This study sought to produce an accurate multi-hazard risk map for a mountainous region of Iran. The study area is in southwestern Iran. The region has experienced numerous extreme natural events in recent decades. This study models the probabilities of snow avalanches, landslides, wildfires, land subsidence, and floods using machine learning models that include support vector machine (SVM), boosted regression tree (BRT), and generalized linear model (GLM). Climatic, topographic, geological, social, and morphological factors were the main input variables used. The data were obtained from several sources. The accuracies of GLM, SVM, and functional discriminant analysis (FDA) models indicate that SVM is the most accurate for predicting landslides, land subsidence, and flood hazards in the study area. GLM is the best algorithm for wildfire mapping, and FDA is the most accurate model for predicting snow avalanche risk. The values of AUC (area under curve) for all five hazards using the best models are greater than 0.8, demonstrating that the model's predictive abilities are acceptable. A machine learning approach can prove to be very useful tool for hazard management and disaster mitigation, particularly for multi-hazard modeling. The predictive maps produce valuable baselines for risk management in the study area, providing evidence to manage future human interaction with hazards.

/pdf/a-machine-learning-framework-for-multi-hazards-modeling-and-36mqqqvsw1.pdf

A machine learning framework for multi-hazards modeling and mapping in a mountainous area.

This conceptual model of avalanche hazard identifies the key components of avalanche hazard and structures them into a systematic, consistent workflow for hazard and risk assessments. The method is applicable to all types of avalanche forecasting operations, and the underlying principles can be applied at any scale in space or time. The concept of an avalanche problem is introduced, describing how different types of avalanche problems directly influence the assessment and management of the risk. Four sequential questions are shown to structure the assessment of avalanche hazard, namely: (1) What type of avalanche problem(s) exists? (2) Where are these problems located in the terrain? (3) How likely is it that an avalanche will occur? and (4) How big will the avalanche be? Our objective was to develop an underpinning for qualitative hazard and risk assessments and address this knowledge gap in the avalanche forecasting literature. We used judgmental decomposition to elicit the avalanche forecasting process from forecasters and then described it within a risk-based framework that is consistent with other natural hazards disciplines.

/pdf/a-conceptual-model-of-avalanche-hazard-3p96g8xof3.pdf

A conceptual model of avalanche hazard

Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.

/pdf/multi-hazard-exposure-mapping-using-machine-learning-26014224bm.pdf

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

The Avalanche Danger Scale is an ordinal, five-level warning system that is a cornerstone of public avalanche information. The system was developed in Europe in 1993, and introduced to North America in 1994. Although both Canada and the United States adopted the system, different descriptors of the danger levels were developed in each country. Fifteen years of practical use revealed numerous deficiencies in this danger scale, most notably a lack of clarity during low probability/high consequence avalanche conditions. In 2005, a group of Canadian and American avalanche forecasters and researchers began to revise the system, with the goal of improving clarity and developing a single standard for North America. Initial explorations to define the problem resulted in more questions and uncovered an almost complete absence of formal underpinnings for the danger scale. The magnitude of the project subsequently changed, and in 2007 the project objectives were clarified as: 1) definitions of avalanche hazard, danger and risk; 2) methodology for assessing avalanche danger; and 3) revisions to the danger scale as a public communication tool. This paper concentrates on the third and final objective, and describes the methods and results of producing the North American Public Avalanche Danger Scale. Emphasis is placed on best practice in warning system design and the principles of risk communication, which helped reshape the avalanche danger scale into a more effective communication tool. The revised danger scale will be implemented across Canada and the United States for the 2010/11 season.

/pdf/the-north-american-public-avalanche-danger-scale-4m8m5hq92d.pdf

The north american public avalanche danger scale

Avisualanche Consulting, Vancouver BC, Canada Canadian Mountain Holidays, Johnsons Landing BC, Canada WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland Tyrolean Avalanche Warning Service, Innsbruck, Austria Canadian Avalanche Centre, Revelstoke BC, Canada Centre d’Etudes de la Neige, Meteo France, Saint Martin d'Heres, France Canadian Avalanche Association, Revelstoke BC, Canada

/pdf/an-international-standard-for-the-exchange-of-snow-profile-46tswwrgbz.pdf

An international standard for the exchange of snow profile information an example for a domain specific application of caaml 5.0

/pdf/a-conceptual-model-of-avalanche-hazard-4demv5axy2.pdf

A Conceptual Model of Avalanche Hazard

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A conceptual model of avalanche hazard

The north american public avalanche danger scale

An international standard for the exchange of snow profile information an example for a domain specific application of caaml 5.0

A Conceptual Model of Avalanche Hazard