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.

Trust in the Police: Cross-country Comparisons

2012 ◽  
pp. 24-47
Author(s):  
V. Gimpelson ◽  
G. Monusova
Keyword(s):  
Public Opinion ◽  
Public Attitudes ◽  
Crime Rates ◽  
Authoritarian Regimes ◽  
Data Sets ◽  
The Public ◽  
Positive Attitudes ◽  
Cross Country ◽  
Police Activity

Using different cross-country data sets and simple econometric techniques we study public attitudes towards the police. More positive attitudes are more likely to emerge in the countries that have better functioning democratic institutions, less prone to corruption but enjoy more transparent and accountable police activity. This has a stronger impact on the public opinion (trust and attitudes) than objective crime rates or density of policemen. Citizens tend to trust more in those (policemen) with whom they share common values and can have some control over. The latter is a function of democracy. In authoritarian countries — “police states” — this tendency may not work directly. When we move from semi-authoritarian countries to openly authoritarian ones the trust in the police measured by surveys can also rise. As a result, the trust appears to be U-shaped along the quality of government axis. This phenomenon can be explained with two simple facts. First, publicly spread information concerning police activity in authoritarian countries is strongly controlled; second, the police itself is better controlled by authoritarian regimes which are afraid of dangerous (for them) erosion of this institution.

Der Binding Waldpreis – Auslöser für waldbaulich-ertragskundliche Forschung | The Binding Forest Award initiates silvicultural and yield research

2011 ◽  
Vol 162 (3) ◽  
pp. 65-70
Author(s):  
Andreas Zingg ◽  
Hansheinrich Bachofen
Keyword(s):  
Forest Ecosystems ◽  
Swiss Federal Institute ◽  
Growth And Yield ◽  
High Quality ◽  
Federal Institute ◽  
Ecological Values ◽  
Prize Award ◽  
Landscape Research ◽  
Selection Forest ◽  
Time Required

Between 1995 and 2008 the granting of the Binding Forest Award led to fresh cooperation between forest owners and research on silviculture, growth and yield at the Swiss Federal Institute for Forest, Snow and Landscape Research. Various topics were treated: a study of the beech coppices in Rothenfluh rapidly made it clear that very little was known about this formerly widespread type of forest management and its consequences. The same was true to a lesser extent for the conversion of rather uniform high forest into selection forest (in Plasselb), and for the selective management of light demanding tree species, such as the oak, in Rheinau. In Boudry, cooperation between practice and research already existed: the prize award here led to new approaches in the production of high quality oak, whilst taking ecological values into account. All these new projects are still in their earliest stages and will call for a great deal of “sustainability”, in both senses of the word, from all those involved. Considering the long periods of time required for the development of forest ecosystems, this is in fact self-evident.

scholarly journals A new pan-European dataset for gridded daily average wind speed based on in-situ observations

2021 ◽  
Author(s):  
Jouke de Baar ◽  
Gerard van der Schrier ◽  
Irene Garcia-Marti ◽  
Else van den Besselaar
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Cross Validation ◽  
Data Sets ◽  
Optimal Prediction ◽  
Auxiliary Variables ◽  
Climate Data ◽  
Ensemble Spread ◽  
Wind Fields

<p><strong>Objective</strong></p><p>The purpose of the European Copernicus Climate Change Service (C3S) is to support society by providing information about the past, present and future climate. For the service related to <em>in-situ</em> observations, one of the objectives is to provide high-resolution (0.1x0.1 and 0.25x0.25 degrees) gridded wind speed fields. The gridded wind fields are based on ECA&D daily average station observations for the period 1970-2020.</p><p><strong>Research question</strong> </p><p>We address the following research questions: [1] How efficiently can we provide the gridded wind fields as a statistically reliable ensemble, in order to represent the uncertainty of the gridding? [2] How efficiently can we exploit high-resolution geographical auxiliary variables (e.g. digital elevation model, terrain roughness) to augment the station data from a sparse network, in order to provide gridded wind fields with high-resolution local features?</p><p><strong>Approach</strong></p><p>In our analysis, we apply greedy forward selection linear regression (FSLR) to include the high-resolution effects of the auxiliary variables on monthly-mean data. These data provide a ‘background’ for the daily estimates. We apply cross-validation to avoid FSLR over-fitting and use full-cycle bootstrapping to create FSLR ensemble members. Then, we apply Gaussian process regression (GPR) to regress the daily anomalies. We consider the effect of the spatial distribution of station locations on the GPR gridding uncertainty.</p><p>The goal of this work is to produce several decades of daily gridded wind fields, hence, computational efficiency is of utmost importance. We alleviate the computational cost of the FSLR and GPR analyses by incorporating greedy algorithms and sparse matrix algebra in the analyses.</p><p><strong>Novelty</strong>   </p><p>The gridded wind fields are calculated as a statistical ensemble of realizations. In the present analysis, the ensemble spread is based on uncertainties arising from the auxiliary variables as well as from the spatial distribution of stations.</p><p>Cross-validation is used to tune the GPR hyper parameters. Where conventional GPR hyperparameter tuning aims at an optimal prediction of the gridded mean, instead, we tune the GPR hyperparameters for optimal prediction of the gridded ensemble spread.</p><p>Building on our experience with providing similar gridded climate data sets, this set of gridded wind fields is a novel addition to the E-OBS climate data sets.</p>

Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection

2004 ◽  
Vol 3 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Minhui Paik ◽  
Yuhong Yang
Keyword(s):  
Gene Expression ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Data Sets ◽  
Weighting Method ◽  
Considerable Uncertainty ◽  
Combined Classifier ◽  
Nearest Neighbor Classifiers

Various discriminant methods have been applied for classification of tumors based on gene expression profiles, among which the nearest neighbor (NN) method has been reported to perform relatively well. Usually cross-validation (CV) is used to select the neighbor size as well as the number of variables for the NN method. However, CV can perform poorly when there is considerable uncertainty in choosing the best candidate classifier. As an alternative to selecting a single “winner," we propose a weighting method to combine the multiple NN rules. Four gene expression data sets are used to compare its performance with CV methods. The results show that when the CV selection is unstable, the combined classifier performs much better.

scholarly journals Robotics: Science and Systems IV

AI Magazine ◽  
2009 ◽  
Vol 30 (4) ◽  
pp. 113
Author(s):  
Oliver Brock ◽  
Jeff Trinkle ◽  
Fabio Ramos
Keyword(s):  
Program Committee ◽  
Early Career ◽  
Swiss Federal Institute ◽  
Robotic Systems ◽  
Single Track ◽  
Federal Institute ◽  
Institute Of Technology

The conference Robotics: Science and Systems was held at the Swiss Federal Institute of Technology (ETH) in Zurich Switzerland, from June 25 to June 28, 2008. More than 280 international researchers attended this single track conference to learn about the most exciting robotics research and most advanced robotic systems. The program committee, led by sixteen area chairs, selected 40 papers out of 163 submissions. The program also included seven invited talks and two early career spotlight presentations. The plenary presentations were complemented by thirteen workshops. 

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