166 research outputs found
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Flash flood simulations for an Egyptian city - mitigation measures and impact of infiltration
Within this work, the impact of mitigation measures and infiltration on flash floods is investigated by using a 2D robust shallow water model including infiltration with the Green-Ampt model. The results show the combined effects of infiltration and mitigation measures as well as the effectiveness of bypass channels in addition to retention basins. Retention basins at appropriate locations could reduce the maximum water depth at critical locations by 23%, while the additional implementation of drainage channels lead to a reduction of 75%, considering also infiltration lead to a further reduction of 97%. If infiltration was considered without mitigation measures, the peak water depth was reduced by 67%. For an exceptional extreme event the measures lead to a reduction of 73% at some locations, while at other locations the overflow from retention basins due to overstraining generated even higher inundations with an increase of 58%
On the processes of renewal of the North Atlantic deep water in the Irminger Sea
Observations of temperature and electrical conductivity by a recording in situ salinometer are discussed in respect oo the physical processes connected with the renewal of North Atlantic deep water. The measured fine structure of the layering suggests that the downward movement of cooled surface water is combined with horizontal mixing down to more than 1000 m depth. This is confirmed by the existence of water elements which have slightly different temperature and salinity. Curves of temperature, conductivity, and salinity and T-S diagrams are shown
Upscaling the shallow water model with a novel roughness formulation
This study presents a novel roughness formulation
to conceptually account for microtopography
and compares it to four existing roughness models from
literature. The aim is to increase the grid size for computational
efficiency, while capturing subgrid scale effects
with the roughness formulation to prevent the loss
in accuracy associated with coarse grids. All roughness
approaches are implemented in the Hydroinformatics
Modeling System and compared with results of
a high resolution shallow water model in three test
cases: rainfall-runoff on an inclined plane with sinewave
shaped microtopography,
ow over an inclined
plane with random microtopography and rainfall-runoff
in a small natural catchment. Although the high resolution
results can not be reproduced exactly by the coarse
grid model, e.g. local details of
ow processes can not
be resolved, overall good agreement between the upscaled models and the high resolution model has been
achieved. The proposed roughness formulation generally
shows the best agreement of all compared models.
It is further concluded that the accuracy increases with
the number of calibration parameters available, however
the calibration process becomes more difficult. Using
coarser grids results in significant speedup in comparison
with the high resolution simulation. In the presented
test cases the speedup varies from 20 up to 2520,
depending on the size and complexity of the test case
and the difference in cell sizes.The authors thank the Alexander von
Humboldt-Foundation for the Humboldt Research Fellowship
granted to Dr. Dongfang Liang.This is the accepted manuscript. The final version is available at http://link.springer.com/article/10.1007%2Fs12665-015-4726-7
Model Integration and Coupling in A Hydroinformatics System
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv
A Mathematical Framework for Agent Based Models of Complex Biological Networks
Agent-based modeling and simulation is a useful method to study biological
phenomena in a wide range of fields, from molecular biology to ecology. Since
there is currently no agreed-upon standard way to specify such models it is not
always easy to use published models. Also, since model descriptions are not
usually given in mathematical terms, it is difficult to bring mathematical
analysis tools to bear, so that models are typically studied through
simulation. In order to address this issue, Grimm et al. proposed a protocol
for model specification, the so-called ODD protocol, which provides a standard
way to describe models. This paper proposes an addition to the ODD protocol
which allows the description of an agent-based model as a dynamical system,
which provides access to computational and theoretical tools for its analysis.
The mathematical framework is that of algebraic models, that is, time-discrete
dynamical systems with algebraic structure. It is shown by way of several
examples how this mathematical specification can help with model analysis.Comment: To appear in Bulletin of Mathematical Biolog
Signatures of polaronic excitations in quasi-one-dimensional LaTiO
The optical properties of quasi-one-dimensional metallic LaTiO are
studied for the polarization along the and axes. With decreasing
temperature modes appear along both directions suggestive for a phase
transition. The broadness of these modes along the conducting axis might be due
to the coupling of the phonons to low-energy electronic excitations across an
energy gap. We observe a pronounced midinfrared band with a temperature
dependence consistent with (interacting) polaron models. The polaronic picture
is corroborated by the presence of strong electron-phonon coupling and the
temperature dependence of the dc conductivity.Comment: 5 pages, 5 figure
Characterization of Reachable Attractors Using Petri Net Unfoldings
International audienceAttractors of network dynamics represent the long-term behaviours of the modelled system. Their characterization is therefore crucial for understanding the response and differentiation capabilities of a dynamical system. In the scope of qualitative models of interaction networks, the computation of attractors reachable from a given state of the network faces combinatorial issues due to the state space explosion. In this paper, we present a new algorithm that exploits the concurrency between transitions of parallel acting components in order to reduce the search space. The algorithm relies on Petri net unfoldings that can be used to compute a compact representation of the dynamics. We illustrate the applicability of the algorithm with Petri net models of cell signalling and regulation networks, Boolean and multi-valued. The proposed approach aims at being complementary to existing methods for deriving the attractors of Boolean models, while being %so far more generic since it applies to any safe Petri net
Reports of the AAAI 2019 spring symposium series
Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have already been involved in fatalities, including a pedestrian; and yet machine learning is unable to explain the contexts within which it operates
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