Snow avalanche hazard modelling of large areas using shallow water numerical methods and GIS

Snow avalanches threaten settlements and roads in steep mountainous areas. Hazard mitigation strategies apply numerical models in combination with GIS-based methods to determine run out distances and pressure maps of snow avalanches in three-dimensional terrain. The snow avalanche modelling system is usually applied to study single avalanche tracks. In this paper we investigate the application of a numerical modelling system for large area hazard analysis. We begin by briefly presenting the depth-averaged equations governing avalanche flow. Then, we describe the statistical and GIS-based methods that are applied to define the initial fracture depths and release areas for snow avalanche modelling. We discuss the calibration of the avalanche model friction coefficients for extreme avalanches in function of altitude, avalanche size and topography. Seven test sites with areas between 100 and 350 km², that are well distributed over the different snow climates and elevation ranges of Switzerland, were used to calibrate the model by comparing the simulation results with historic avalanche events and existing avalanche hazard maps. We then show how the avalanche modelling system was applied over the mountainous region of Switzerland (25,000 km²) to delineate forests with protective function against avalanches.     

Interactive Generation of Time‐evolving,Snow‐Covered Landscapes with Avalanches

We introduce a novel method for interactive generation of visually consistent, snow‐covered landscapes and provide control of their dynamic evolution over time. Our main contribution is the real‐time phenomenological simulation of avalanches and other user‐guided events, such as tracks left by Nordic skiing, which can be applied to interactively sculpt the landscape. The terrain is modeled as a height field with additional layers for stable, compacted, unstable, and powdery snow, which behave in combination as a semi‐viscous fluid. We incorporate the impact of several phenomena, including sunlight, temperature, prevailing wind direction, and skiing activities. The snow evolution includes snow‐melt and snow‐drift, which affect stability of the snow mass and the probability of avalanches. A user can shape landscapes and their evolution either with a variety of interactive brushes, or by prescribing events along a winter season time‐line. Our optimized GPU‐implementation allows interactive updates of snow type and depth across a large (10 × 10 km) terrain, including real‐time avalanches, making this suitable for visual assets in computer games. We evaluate our method through perceptual comparison against exiting methods and real snow‐depth data.     

On the complexity of sandpile critical avalanches

In this work, we study The Abelian Sandpile Model from the point of view of computational complexity. We begin by studying the length distribution of sandpile avalanches triggered by the addition of two critical configurations: we prove that those avalanches are long on average, their length is bounded below by a constant fraction of the length of the longest critical avalanche which is, in most of the cases, superlinear. At the end of the paper we take the point of view of computational complexity, we analyze the algorithmic hardness of the problem consisting in computing the addition of two critical configurations, we prove that this problem is P complete, and we prove that most algorithmic problems related to The Abelian Sandpile Model are NC reducible to it.     

The effect of the earth pressure coefficients on the runout of granular material

In the framework of a better territory risk assessment and decision making, numerical simulation can provide a useful tool for investigating the propagation phase of phenomena involving granular material, like rock avalanches, when realistic geological contexts are considered.Among continuum mechanics models, the numerical model SHWCIN uses the depth averaged Saint Venant approach, in which the avalanche thickness (H) is very much smaller than its extent parallel to the bed (L). The material is assumed to be incompressible and the mass and the momentum equations are written in a depth averaged form.The SHWCIN code, based on the hypothesis of isotropy of normal stresses (σ_xx = σ_yy = σ_zz), has been modified (new code: RASH^3D) in order to allow for the assumption of anisotropy of normal stresses (σ_xx = Kσ_zz; σ_yy = Kσ_zz).A comparison among the results obtained by assuming isotropy or anisotropy is given through the back analysis of a set of laboratory experiments [Gray, J.M.N.T., Wieland, M., Hutter, K., 1999. Gravity-driven free surface flow of granular avalanches over complex basal topography. Proceedings of the Royal Society of London, Series A 455(1841)] and of a case history of rock avalanche (Frank slide, Canada).The carried out simulations have also underlined the importance of using a different earth pressure coefficient value (K) for directions of convergence and of divergence of the flux.     

Design and implementation of the Node Identity Internetworking Architecture

The Internet Protocol (IP) has been proven very flexible, being able to accommodate all kinds of link technologies and supporting a broad range of applications. The basic principles of the original Internet architecture include end-to-end addressing, global routeability and a single namespace of IP addresses that unintentionally serves both as locators and host identifiers. The commercial success and widespread use of the Internet have lead to new requirements, which include Internetworking over business boundaries, mobility and multi-homing in an untrusted environment. Our approach to satisfy these new requirements is to introduce a new Internetworking layer, the node identity layer. Such a layer runs on top of the different versions of IP, but could also run directly on top of other kinds of network technologies, such as MPLS and 2G/3G PDP contexts. This approach enables connectivity across different communication technologies, supports mobility, multi-homing, and security from ground up. This paper describes the Node Identity Architecture in detail and discusses the experiences from implementing and running a prototype.     

Observed and modelled effects of ice lens formation on passive microwave brightness temperatures over snow covered tundra

Immediately before an April 2007 snow survey and passive microwave radiometer field campaign in the Northwest Territories, Canada, a rain-on-snow event deposited a thin (~ 3 mm) continuous layer of ice on the surface of the snowpack. At eight sites the brightness temperature (T_b) of the undisturbed snow pack was measured with a multi-frequency dual polarization (6.9, 19, 37, and 89 GHz) ground based radiometer system. The ice lens was then carefully removed and the T_bs were measured again. The individual V-pol channels and the 37 V − 19 V difference were largely unaffected by the presence of the ice lens, exhibiting a systematic shift of about 3 K. In comparison, the ice lens had a considerable effect on the H-pol T_b at all frequencies, with a mean difference (ice lens present − ice lens removed) of − 9 K (± 5.3 K) at 6.9 GHz, − 40 K (± 11.3 K) at 19 GHz, − 33 K (± 7.6 K) at 37 GHz, and − 19 K (± 8.0 K) at 89 GHz. The effect of the ice lens on H-pol measurements was also observed with spaceborne data from the Advanced Microwave Scanning Radiometer (AMSR-E) satellite data.Simulations of T_b were produced for each site using a new two layer formulation of the Helsinki University of Technology (HUT) snow emission model. The ice lens was used as the top layer and the underlying snowpack considered as a homogenous second layer. The agreement between observations and simulations was variable, with agreement strongest at 19 GHz. A comparison with simulations produced using the Microwave Emission Model of Layered Snowpacks (MEMLS) suggests HUT model uncertainty is related not to the ice lens, but to difficulties in simulating emission from deep snow. Overall, the observations and simulations suggest H-pol measurements are capable of detecting new ice layers across the tundra snowpack, while V-pol measurements are more appropriate for snow water equivalent (SWE) retrievals due to their relative insensitivity to ice layers.     

Connections among nonlinearity,avalanche and correlation immunity

Nonlinear Boolean functions play an important role in the design of block ciphers, stream ciphers and one-way hash functions. Over the years researchers have identified a number of indicators that forecast nonlinear properties of these functions. Studying the relationships among these indicators has been an area that has received extensive research. The focus of this paper is on the interplay of three notable nonlinear indicators, namely nonlinearity, avalanche and correlation immunity. We establish, for the first time, an explicit and simple lower bound on the nonlinearity N_f of a Boolean function f of n variables satisfying the avalanche criterion of degree p, namely, N_f⩾2ⁿ⁻¹−2^{n−1−(1/2)p}. We also identify all the functions whose nonlinearity attains the lower bound. As a further contribution of this paper, we prove that except for very few cases, the sum of the degree of avalanche and the order of correlation immunity of a Boolean function of n variables is at most n−2. The new results obtained in this work further highlight the significance of the fact that while avalanche property is in harmony with nonlinearity, both go against correlation immunity.     

On construction of the entropy transport model based on the formalism of nonextensive statistics

A new approach to the construction of transport entropy models of distribution based on the formalism of nonextensive statistics is proposed. As an example, a simple entropy model is built for single-purpose travels and for a homogenous group of cars. This approach allows simulation of more complicated non-Gibbs distributions. The fundamental principle of the research is the Tsallis nonextensive entropy and degree distributions dependent on the actual number q which is the measure of nonadditive complex socioeconomic systems.     

A collaborative user-centered framework for recommending items in Online Social Networks

Recommender Systems are more and more playing an important role in our life, representing useful tools helping users to find “what they need” from a very large number of candidates and supporting people in making decisions in various contexts: what items to buy, which movie to watch, or even who they can invite to their social network, etc. In this paper, we propose a novel collaborative user-centered recommendation approach in which several aspects related to users and available in Online Social Networks – i.e. preferences (usually in the shape of items’ metadata), opinions (textual comments to which it is possible to associate a sentiment), behavior (in the majority of cases logs of past items’ observations made by users), feedbacks (usually expressed in the form of ratings) – are considered and integrated together with items’ features and context information within a general framework that can support different applications using proper customizations (e.g., recommendation of news, photos, movies, travels, etc.). Experiments on system accuracy and user satisfaction in several domains shows how our approach provides very promising and interesting results.     

Hybrid user intention modeling to diversify dialog simulations

This paper proposes a novel user intention simulation method which is data-driven but can integrate diverse user discourse knowledge to simulate various types of user behaviors. A method of data-driven user intention modeling based on logistic regression is introduced in the Markov logic framework. Human dialog knowledge is designed into two layers, domain and discourse knowledge, and integrated with the data-driven model in generation time. Three types of user knowledge, i.e., cooperative, corrective and self-directing, are designed and integrated to generate behaviors of corresponding user-types. In experiments to investigate the patterns of simulated users, the approach successfully generated cooperative, corrective and self-directing user intention patterns.     

Size distribution and waiting times for the avalanches of the Cell Network Model of Fracture

The Cell Network Model is a fracture model recently introduced that resembles the microscopical structure and drying process of the parenchymatous tissue of the Bamboo Guadua angustifolia. The model exhibits a power-law distribution of avalanche sizes, with exponent −3.0 when the breaking thresholds are randomly distributed with uniform probability density. Hereby we show that the same exponent also holds when the breaking thresholds obey a broad set of Weibull distributions, and that the humidity decrements between successive avalanches (the equivalent to waiting times for this model) follow in all cases an exponential distribution. Moreover, the fraction of remaining junctures shows an exponential decay in time. In addition, introducing partial breakings and cumulative damages induces a crossover behavior between two power-laws in the histogram of avalanche sizes. This results support the idea that the Cell Network Model may be in the same universality class as the Random Fuse Model.     

Highly nonlinear balanced Boolean functions with good local and global avalanche characteristics

Here we deal with an interesting subset of n-variable balanced Boolean functions which satisfy strict avalanche criteria. These functions achieve the sum-of-square indicator value (a measure for global avalanche criteria) strictly less than 2²ⁿ⁺¹ and nonlinearity strictly greater than 2ⁿ⁻¹−2^⌊n/2⌋. These parameters are currently best known. Moreover, these functions do not possess any nonzero linear structure. The technique involves a well-known simple construction coupled with very good initial functions obtained by computer search, which were not known earlier.     

Using mobile beacons to locate sensors in obstructed environments

Locating sensors in an indoor environment is a challenging problem due to the insufficient distance measurements caused by short ultrasound range and the incorrect distance measurements caused by multipath effect of ultrasound. In this paper, we propose a virtual ruler approach, in which a vehicle equipped with multiple ultrasound beacons travels around the area to measure distances between pairwise sensors. Virtual Ruler can not only obtain sufficient distances between pairwise sensors, but can also eliminate incorrect distances in the distance measurement phase of sensor localization. We propose to measure the distance between pairwise sensors from multiple perspectives using the virtual ruler and filter incorrect values through a statistical approach. By assigning measured distances with confidence values, the localization algorithm can intelligently localize each sensor based on high confidence distances, which greatly improves localization accuracy. Our performance evaluation shows that the proposed approach can achieve better localization results than previous approaches in an indoor environment.     

A feasible method for fractional snow cover mapping in boreal zone based on a reflectance model

A feasible method for mapping the fraction of Snow Covered Area (SCA) in the boreal zone is presented. The method (SCAmod) is based on a semi-empirical model, where three reflectance contributors (wet snow, snow-free ground and forest canopy), interconnected by an effective canopy transmissivity and SCA, constitute the observed reflectance from the target area. Given the reflectance observation, SCA is solved from the model. The predetermined values for the reflectance contributors can be adjusted to an optional wavelength region, which makes SCAmod adaptable to various optical sensors. The effective forest canopy transmissivity specifies the effect of forests on the local reflectance observation; it is estimated using Earth observation data similar to that employed in the actual SCA estimation. This approach enables operational snow mapping for extensive areas, as auxiliary forest data are not needed.Our study area covers 404 000 km², comprising all drainage basins of Finland (with an average area of 60 km²) and some transboundary drainage basins common with Russia, Norway and Sweden. Applying SCAmod to NOAA/AVHRR cloud-free data acquired during melting periods 2001-2003, we estimated the areal fraction of snow cover for all the 5845 basins. The validation against in situ SCA from the Finnish snow course network indicates that with SCAmod, 15% (absolute SCA-units) accuracy for SCA is gained. Good results were also obtained from the validation against snow cover information provided by the Finnish weather station network, for example, 94% of snow-free and fully snow-covered basins were recognized. A general formula for deriving the statistical accuracy of SCA estimates provided by SCAmod is presented, complemented by the results when the AVHRR data are employed.Snow melting in Finland has been operatively monitored with SCAmod in Finnish Environment Institute (SYKE) since year 2001. The SCA estimates have been assimilated to the Finnish national hydrological modelling and forecasting system since 2003, showing a substantial improvement in forecasts.     

Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams

Real-time surveillance systems, telecommunication systems, and other dynamic environments often generate tremendous (potentially infinite) volume of stream data: the volume is too huge to be scanned multiple times. Much of such data resides at rather low level of abstraction, whereas most analysts are interested in relatively high-level dynamic changes (such as trends and outliers). To discover such high-level characteristics, one may need to perform on-line multi-level, multi-dimensional analytical processing of stream data. In this paper, we propose an architecture, called stream_cube, to facilitate on-line, multi-dimensional, multi-level analysis of stream data. For fast online multi-dimensional analysis of stream data, three important techniques are proposed for efficient and effective computation of stream cubes. First, a tilted time frame model is proposed as a multi-resolution model to register time-related data: the more recent data are registered at finer resolution, whereas the more distant data are registered at coarser resolution. This design reduces the overall storage of time-related data and adapts nicely to the data analysis tasks commonly encountered in practice. Second, instead of materializing cuboids at all levels, we propose to maintain a small number of critical layers. Flexible analysis can be efficiently performed based on the concept of observation layer and minimal interesting layer. Third, an efficient stream data cubing algorithm is developed which computes only the layers (cuboids) along a popular path and leaves the other cuboids for query-driven, on-line computation. Based on this design methodology, stream data cube can be constructed and maintained incrementally with a reasonable amount of memory, computation cost, and query response time. This is verified by our substantial performance study. Recommended by: Ahmed Elmagarmid     

Accessing Video Contents through Key Objects over IP

In order to support content-based video database access over the Internet Protocol (IP), achieving the following objectives are important: (i) video query by a representative object (key object) or some statistical characterization of the target contents, (ii) bandwidth-efficient browsing over IP, and (iii) scalable and user-centric video transmission over a heterogeneous and variable-bandwidth network. We present a video object extraction and scalable coding system designed to meet the above objectives. In our system, key objects of meaning to video database users are generated via a human-computer-interaction procedure, and are tracked across frames. Given a key object, an algorithm classifies a subset of its VOPs as key VOPs. This subset forms the basis of a highly bandwidth-efficient base layer for supporting activities such as browsing and refining queries. Over the base layer, a number of enhancement layers can be defined to progressively increase the spatial and temporal resolutions of retrieved video. It is expected that heterogeneous users can subscribe to different numbers of the enhancement layers according to their own conditions, such as access authorization, available connection bandwidth, and quality preference.     

Evaluation of emission from snow-covered ground for passive microwave remote sensing

This article investigates emission behaviour at frequencies of 18.7, 36.5 and 89 GHz and an incidence angle of 55° over a snow-covered surface at the local scale observation site in Fraser, CO, USA, using both one-layer and two-layer emission models. The models employ the matrix doubling approach to implement the radiative-transfer equation based on dense media theory and the advanced integral equation model. When compared to Ground-Based Passive Microwave Radiometer (GBMR-7) observation on 21 February 2003, both the models could simulate the observed brightness temperature well, but the polarization difference between the observation and the models was smaller for the two-layer emission model than the one-layer model. In addition, we successfully interpreted the emission magnitude and polarization separation of a snow-removed surface by incorporating a Mie scattering transition layer above the soil medium. In this work, we also demonstrated the effect of snow fraction on the brightness temperature difference at 18.7 and 36.5 GHz over a snow-covered surface with the field observation. In conclusion, we demonstrate the snow impact on soil surface with snow depth (SD) and snow fraction variation through modelling and in situ data.