scholarly journals A nearest-neighbour model for forecasting skier-triggered dry-slab avalanches on persistent weak layers in the Columbia Mountains, Canada

2004 ◽  
Vol 38 ◽  
pp. 166-172 ◽  
Author(s):  
Antonia Zeidler ◽  
Bruce Jamieson
Keyword(s):  
Classification Tree ◽  
Regional Scale ◽  
Rank Correlation ◽  
Stability Index ◽  
Important Variable ◽  
Slab Thickness ◽  
Nearest Neighbour ◽  
Avalanche Forecasting ◽  
Columbia Mountains ◽  
Snowpack Properties

AbstractNearest-neighbour models for avalanche forecasting have made little use of snowpack properties; however, slab thickness (H), slab load (Load) and a skier stability index (Sk38) have proven useful for regional avalanche forecasting in the Columbia Mountains, western Canada. This study explores 21 meteorological, snowpack and elaborated variables including Sk38, H and Load. A daily skier instability index (DSI) is developed as a response variable using skier-triggered avalanche activity on persistent weak layers and stability ratings at the end of the day. In rank correlation analysis, Sk38, Load, previous avalanche activity, H and some meteorological variables were highly ranked. The physical explanations are discussed. In classification-tree analysis, Sk38 was ranked as the most important variable and used in the development of the tree structure along with Load. Besides Sk38 and Load, snowpack thickness, the number of previously triggered avalanches and H have potential to predict DSI. Further we included once all 21 variables, and once all variables except Sk38, H and Load in nearest-neighbour models. Comparing the performance of these models shows that Sk38 along with Load and H have high potential to forecast the DSI on a regional scale.

scholarly journals The Assessment of the Innovativeness of a New Medicine in Italy

2021 ◽  
Vol 8 ◽  
Author(s):  
Filomena Fortinguerra ◽  
Serena Perna ◽  
Roberto Marini ◽  
Alessandra Dell'Utri ◽  
Maurizio Trapanese ◽  
...  
Keyword(s):  
Decision Process ◽  
Classification Tree ◽  
Orphan Drugs ◽  
Important Variable ◽  
Medical Product ◽  
Chi Square ◽  
New Approach ◽  
Exact Test ◽  
Therapeutic Value

Objectives: Starting from April 2017, the Italian Medicine Agency (AIFA) has approved new criteria for defining any new medicinal product with an innovative indication. The purpose of the study is to analyze the activity of innovativeness evaluation according to the new approach, to estimate the weight of each criterion considered for innovativeness definition, and to evaluate how the new approach works in terms of consistency and reproducibility.Methods: A retrospective analysis was performed on the final reports evaluating the drug innovativeness assessment published on the AIFA's website between April 2017 and January 2021. Descriptive statistics, chi-square test, whether the conditions were respected, or Fisher's exact test was used to explore the association between characteristics of drugs and the innovativeness status and the association between the three criteria. Profiles of the decision process and their relationship with innovativeness response were described. In order to evaluate the weight of each criterion in predicting the innovativeness status, a Classification Tree (CT) algorithm was applied.Results: Overall, of the 109 published drugs reports, 37 (33.9%) were recognized as fully innovative, 29 (26.6%) were considered conditionally innovative, while for 43 (39.4%) reports innovativeness was not recognized. Considering the three criteria of the decision process, the added therapeutic value was the only criterion statistically associated with a drug's degree of innovation (p < 0.001). The therapeutic need and the quality of clinical evidence were statistically associated (p = 0.008) even if only a mild association was observed. The added therapeutic value was the most important variable in predicting the innovativeness status according to the classification tree (CT) model applied, achieving an accuracy of 89.4%. No difference was found between orphans and non-orphan drugs or oncological and non-oncological drugs.Discussion: The added therapeutic value is the most important criterion of the multidimensional approach for the innovativeness status definition of a new medical product. A mild association was found between the therapeutic need and the quality of evidence. Overall, similar decision profiles bring the same evaluation of innovativeness status, indicating a good consistency and reproducibility between decisions.

scholarly journals Nearest neighbour models for local and regional avalanche forecasting

2002 ◽  
Vol 2 (3/4) ◽  
pp. 247-253 ◽  
Author(s):  
M. Gassner ◽  
B. Brabec
Keyword(s):  
Cross Validation ◽  
Local Scale ◽  
Swiss Federal Institute ◽  
Hazard Evaluation ◽  
Data Sets ◽  
Nearest Neighbour ◽  
Avalanche Forecasting ◽  
Federal Institute ◽  
Cross Country ◽  
Avalanche Warning

Abstract. This paper presents two avalanche forecasting applications NXD2000 and NXD-REG which were developed at the Swiss Federal Institute for Snow and Avalanche Re-search (SLF). Even both are based on the nearest neighbour method they are targeted to different scales. NXD2000 is used to forecast avalanches on a local scale. It is operated by avalanche forecasters responsible for snow safety at snow sport areas, villages or cross country roads. The area covered ranges from 10 km2 up to 100 km2 depending on the climatological homogeneity. It provides the forecaster with ten most similar days to a given situation. The observed avalanches of these days are an indication of the actual avalanche danger. NXD-REG is used operationally by the Swiss avalanche warning service for regional avalanche forecasting. The Nearest Neighbour approach is applied to the data sets of 60 observer stations. The results of each station are then compiled into a map of current and future avalanche hazard. Evaluation of the model by cross-validation has shown that the model can reproduce the official SLF avalanche forecasts in about 52% of the days.

scholarly journals Integrating passive acoustic and visual data to model spatial patterns of occurrence in coastal dolphins

2014 ◽  
Vol 72 (2) ◽  
pp. 651-660 ◽  
Author(s):  
Paul M. Thompson ◽  
Kate L. Brookes ◽  
Line S. Cordes
Keyword(s):  
Spatial Variation ◽  
Spatial Patterns ◽  
Classification Tree ◽  
Bottlenose Dolphins ◽  
Regional Scale ◽  
Habitats Directive ◽  
Line Transect ◽  
Species Identity ◽  
Passive Acoustic ◽  
Survey Effort

Abstract Fine-scale information on the occurrence of coastal cetaceans is required to support regulation of offshore energy developments and marine spatial planning. In particular, the EU Habitats Directive requires an understanding of the extent to which animals from Special Areas of Conservation (SAC) use adjacent waters, where survey effort is often sparse. Designing survey regimes that can be used to support these assessments is especially challenging because visual sightings are expected to be rare in peripheral parts of a population's range. Consequently, even intensive visual line-transect surveys can result in few encounters. Static passive acoustic monitoring (PAM) provides new opportunities to extend survey effort by using echolocation click detections to quantify levels of occurrence of coastal dolphins, but this does not provide information on species identity. In NE Scotland, assessments of proposed offshore energy developments required information on spatial patterns of occurrence of bottlenose dolphins in waters in and next to the Moray Firth SAC. Here, we illustrate how this can be achieved by integrating data from broad-scale PAM arrays with presence-only data from visual surveys. Generalized estimating equations were used with PAM data to model the occurrence of dolphins in relation to depth, distance to coast, slope, and sediment, and to predict the spatial variation in the cumulative occurrence of all dolphin species across a 4 × 4 km grid of the study area. Classification tree analysis was then applied to available visual sightings data to estimate the likely species identity of dolphins sighted in each grid cell in relation to local habitat. By multiplying these probabilities, it was possible to provide advice on spatial variation in the probability of encountering bottlenose dolphins from this protected population at a regional scale, complementing data from surveys that estimate average density or overall abundance within a region.

scholarly journals Modeling spatially distributed snow instability at a regional scale using Alpine3D

2021 ◽  
pp. 1-16
Author(s):  
Bettina Richter ◽  
Jürg Schweizer ◽  
Mathias W. Rotach ◽  
Alec van Herwijnen
Keyword(s):  
Laser Scanning ◽  
Meteorological Data ◽  
Regional Scale ◽  
Winter Season ◽  
Horizontal Resolution ◽  
Distributed Modeling ◽  
Avalanche Forecasting ◽  
Weak Layer ◽  
Snow Stratigraphy ◽  
Spatially Distributed

Abstract Assessing the avalanche danger level requires snow stratigraphy and instability data. As such data are usually sparse, we investigated whether distributed snow cover modeling can be used to provide information on spatial instability patterns relevant for regional avalanche forecasting. Using Alpine3D, we performed spatially distributed simulations to evaluate snow instability for the winter season 2016–17 in the region of Davos, Switzerland. Meteorological data from automatic weather stations were interpolated to 100 m horizontal resolution and precipitation was scaled with snow depth measurements from airborne laser scanning. Modeled snow instability metrics assessed for two different weak layers suggested that the weak layer closer to the snow surface was more variable. Initially, it was less stable than the weak layer closer to the ground, yet it stabilized faster as the winter progressed. In spring, the simulated snowpack on south-facing slopes stabilized faster than on north-facing slopes, in line with the regional avalanche forecast. In the winter months January to March 2017, simulated instability metrics did not suggest that the snowpack on south-facing slopes was more stable, as reported in the regional avalanche forecast. Although a validation with field data is lacking, these model results still show the potential and challenges of distributed modeling for supporting operational avalanche forecasting.

Machine learning as a tool for avalanche forecasting

2020 ◽  
Author(s):  
Martin Hendrick ◽  
Cristina Pérez-Guillén ◽  
Alec van Herwijnen ◽  
Jürg Schweizer
Keyword(s):  
Machine Learning ◽  
Winter Season ◽  
Swiss Alps ◽  
Machine Learning Techniques ◽  
Mountain Areas ◽  
Avalanche Forecasting ◽  
Clustering And Classification ◽  
Automated Tools ◽  
Snowpack Properties ◽  
Snow Precipitation

<p>Assessing and forecasting avalanche hazard is crucial for the safety of people and infrastructure in mountain areas. Over 20 years of data covering snow precipitation, snowpack properties, weather, on-site observations, and avalanche danger has been collected in the context of operational avalanche forecasting for the Swiss Alps. The quality and breadth of this dataset makes it suitable for machine learning techniques.</p><p>Forecasters mainly process a huge and redundant dataset "manually" to produce daily avalanche bulletins during the winter season. The purpose of this work is to provide the forecasters automated tools to support their work. </p><p>By combining clustering and classification algorithms, we are able to reduce the amount of information that needs to be processed and identify relevant weather and snow patterns that characterize a given avalanche situation.</p>

scholarly journals Shear frame stability parameters for large-scale avalanche forecasting

1993 ◽  
Vol 18 ◽  
pp. 268-273 ◽  
Author(s):  
J.B. Jamieson ◽  
C.D. Johnston
Keyword(s):  
Shear Strength ◽  
Large Scale ◽  
Normal Load ◽  
Meteorological Data ◽  
Stability Index ◽  
Stability Parameter ◽  
Stability Parameters ◽  
Avalanche Forecasting ◽  
Study Plot ◽  
Shear Frame

During the winters of 1990, 1991 and 1992, a field study of stability parameters for forecasting slab avalanches was conducted in the Cariboo and Monashee mountains of western Canada. In a level study plot at 1900 m and on nearby slopes, the shear strength of the weak snowpack layer judged most likely to cause slab avalanches was measured with a 0.025 m2 shear frame and a force gauge. Based on the ratio of shear strength to stress due to the snow load overlying the weak layer, a simple stability parameter and a more theoretically based stability index which corrects the strength for normal load were calculated. These stability parameters are compared with avalanche activity reported for the same day within approximately 30 km of the study plot. Each stability parameter is assessed on the basis of the number of days that it successfully predicted one or more potentially harmful avalanches and the number of days that it successfully predicted no potentially harmful avalanches. Both parameters predicted correctly on at least 75% of the 70 days they were evaluated. The simpler empirical stability parameter worked as well as the one that corrects strength for normal load. For large-scale forecasting of dry-snow slab avalanches, shear frame stability parameters appear to be a useful addition to meteorological data, snowpack observations and slope tests.

scholarly journals Independent evaluation of the SNODAS snow depth product using regional scale LiDAR-derived measurements

2014 ◽  
Vol 8 (3) ◽  
pp. 3141-3170
Author(s):  
A. Hedrick ◽  
H.-P. Marshall ◽  
A. Winstral ◽  
K. Elder ◽  
S. Yueh ◽  
...  
Keyword(s):  
Snow Depth ◽  
Regional Scale ◽  
Validation Dataset ◽  
Model Framework ◽  
Validation Data ◽  
Entire Study ◽  
Independent Validation ◽  
Physically Based ◽  
Snow Model ◽  
Snowpack Properties

Abstract. Repeated Light Detection and Ranging (LiDAR) surveys are quickly becoming the de facto method for measuring spatial variability of montane snowpacks at high resolution. This study examines the potential of a 750 km2 LiDAR-derived dataset of snow depths, collected during the 2007 northern Colorado Cold Lands Processes Experiment (CLPX-2), as a validation source for an operational hydrologic snow model. The SNOw Data Assimilation System (SNODAS) model framework, operated by the US National Weather Service, combines a physically-based energy-and-mass-balance snow model with satellite, airborne and automated ground-based observations to provide daily estimates of snowpack properties at nominally 1 km resolution over the coterminous United States. Independent validation data is scarce due to the assimilating nature of SNODAS, compelling the need for an independent validation dataset with substantial geographic coverage. Within twelve distinctive 500 m × 500 m study areas located throughout the survey swath, ground crews performed approximately 600 manual snow depth measurements during each of the CLPX-2 LiDAR acquisitions. This supplied a dataset for constraining the uncertainty of upscaled LiDAR estimates of snow depth at the 1 km SNODAS resolution, resulting in a root-mean-square difference of 13 cm. Upscaled LiDAR snow depths were then compared to the SNODAS-estimates over the entire study area for the dates of the LiDAR flights. The remotely-sensed snow depths provided a more spatially continuous comparison dataset and agreed more closely to the model estimates than that of the in situ measurements alone. Finally, the results revealed three distinct areas where the differences between LiDAR observations and SNODAS estimates were most drastic, suggesting natural processes specific to these regions as causal influences on model uncertainty.

scholarly journals United States banking stability: An explanation through machine learning

2020 ◽  
Vol 15 (4) ◽  
pp. 137-149
Author(s):  
José Alejandro Fernández Fernández
Keyword(s):  
United States ◽  
Machine Learning ◽  
Interest Rates ◽  
Financial Stability ◽  
Ad Hoc ◽  
Stability Index ◽  
The United States ◽  
Important Variable ◽  
Bank Stability ◽  
Bank Solvency

In this paper, an analysis of the prediction of bank stability in the United States from 1990 to 2017 is carried out, using bank solvency, delinquency and an ad hoc bank stability indicator as variables to measure said stability. Different machine learning assembly models have been used in the study, a random forest is developed because it is the most accurate of all those tested. Another novel element of the work is the use of partial dependency graphs (PDP) and individual conditional expectation curves (ICES) to interpret the results that allow observing for specific values how the banking variables vary, when the macro-financial variables vary.It is concluded that the most determining variables to predict bank solvency in the United States are interest rates, specifically the mortgage rate and the 5 and 10-year interest rates of treasury bonds, reducing solvency as these rates increase. For delinquency, the most important variable is the unemployment rate in the forecast. The financial stability index is made up of the normalized difference between the two factors obtained, one for solvency and the other for delinquency. The index prediction concludes that stability worsens as BBB corporate yield increases.

scholarly journals Meteorological, elevation, and slope effects on surface hoar formation

2015 ◽  
Vol 9 (2) ◽  
pp. 1857-1885 ◽  
Author(s):  
S. Horton ◽  
M. Schirmer ◽  
B. Jamieson
Keyword(s):  
Snow Cover ◽  
Weather Prediction ◽  
Regional Scale ◽  
Absolute Humidity ◽  
Numerical Weather Prediction Model ◽  
Weather Data ◽  
Cold Surface ◽  
Columbia Mountains ◽  
Horizontal Grid

Abstract. Failure in layers of buried surface hoar crystals (frost) can cause hazardous snow slab avalanches. Surface hoar crystals form on the snow surface and are sensitive to micro-meteorological conditions. In this study, the role of meteorological and terrain factors were investigated for three surface hoar layers in the Columbia Mountains of Canada. The distribution of crystals was observed over different elevations and aspects during 20 days of field observations. The same layers were modelled on a 2.5 km horizontal grid by forcing the snow cover model SNOWPACK with forecast weather data from a numerical weather prediction model. The moisture content of the air (i.e. absolute humidity) had the largest impact on modelled surface hoar growth, with warm and moist air being favourable. Surface hoar was most developed at certain elevation bands, usually corresponding to elevations with warm humid air, light winds, and cold surface temperatures. SNOWPACK simulations on virtual slopes systematically predicted smaller surface hoar on south-facing slopes. In the field, a complex combination of surface hoar and sun crusts were observed, suggesting the model did not adequately resolve the surface energy balance on slopes. Overall, a coupled weather–snow cover model could benefit avalanche forecasters by predicting surface hoar layers on a regional scale over different elevation bands.

Sign in / Sign up

Export Citation Format

Share Document