Enschede, Netherlands
Enschede, Netherlands

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Das I.,University of Twenty | Das I.,Indian Institute of Remote Sensing | Das I.,ITC Inc | Kumar G.,Indian Institute of Remote Sensing | And 3 more authors.
Environmental Monitoring and Assessment | Year: 2011

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment. © 2010 The Author(s).


Schutte J.H.,University of Twenty | Wijnant Y.H.,University of Twenty | De Boer A.D.,University of Twenty
Acta Acustica united with Acustica | Year: 2015

The horn effect is known as an important amplification mechanism in tyre/road noise. The name is referring to the geometry between tyre and road surface which resembles an exponential horn. The horn effect is a common subject for both experimental and numerical research. Contrary to previous studies which considered point sources, this paper focusses on the horn effect by simulated tyre vibrations. The amplification of acoustic pressure, however, depends largely on the location of the observer. The sound power can be used as a measure for the horn effect which is independent on the point of observation. In this paper, the sound radiation problem is solved using the boundary element method (BEM). First, a case study considering equivalent point sources is used to validate the accuracy of the boundary element model and solver using experimental results. Next, the vibrations of tyres rolling on textured road surfaces are investigated numerically. The computed tyre vibrations are used to study the horn effect using different tyre designs. The amplification by horn effect is determined by the combined tyre/road geometry and the distribution of the noise. The current method may be used to systematically study the influence of the horn effect, for example, during tyre development. © S. Hirzel Verlag EAA.


Yusof N.,University of Technology Malaysia | Zurita-Milla R.,University of Twenty | Kraak M.-J.,University of Twenty | Retsios B.,University of Twenty
International Journal of Geographical Information Science | Year: 2016

Wind speed and direction vary over space and time due to the interactions between different pressures and temperature gradients within the atmospheric layers. Near the earth’s surface, these interactions are modulated by topography and artificial structures. Hence, characterizing wind behaviour over large areas and long periods is a complex but essential task for various energy-related applications. In this study, we present a novel approach to discover wind patterns by integrating sequential pattern mining and interactive visualization techniques. The approach relies on the use of the Linear time Closed pattern Miner sequence algorithm in conjunction with a time sliding window that allows the discovery of all sequential patterns present in the data. These patterns are then visualized using integrated 2D and 3D coordinated multiple views and visually explored to gain insight into the characteristics of the wind from a spatial, temporal and attribute (type of wind pattern) point of view. This proposed approach is used to analyse 10 years of hourly wind speed and direction data for 29 weather stations in the Netherlands. The results show that there are 15 main sequential patterns in the data. The spatial task shows that weather stations located in the same region do not necessarily experience similar wind pattern. For within the selected time interval, similar wind patterns can be observed in different stations and in the same station at different times of occurrence. The attribute task discovered that the repetitive occurrences of chosen pattern indicate as regular wind behaviour at different weather stations that persisted continuously over time. The results of these tasks show that the proposed interactive discovery facilitates the understanding of wind dynamics in space and time. © 2016 Taylor & Francis


PubMed | University of Twenty
Type: Journal Article | Journal: Environmental monitoring and assessment | Year: 2011

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment.

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