Parallel and evolutionary applications to cellular automata models for mitigation of lava flow invasions
Creato da
Filippone, Giuseppe
Spataro, William
D'Ambrosio, Donato
Marocco, Davide
Leone, Nicola
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Dottorato di Ricerca in Matematica e Informatica, XXVI Ciclo 2013; In the lava
ow mitigation context, the determination of areas exposed to
volcanic risk is crucial for diminishing consequences in terms of human causalities
and damages of material properties. In order to mitigate the destructive
e ects of lava
ows along volcanic slopes, the building and positioning of arti
cial barriers is fundamental for controlling and slowing down the lava
ow
advance.
In this thesis, a decision support system for de ning and optimizing volcanic
hazard mitigation interventions is proposed. The Cellular Automata
numerical model SCIARA-fv2 for simulating lava
ows at Mt Etna (Italy)
and Parallel Genetic Algorithms (PGA) for optimizing protective measures
construction by morphological evolution have been considered.
In particular, the PGA application regarded the optimization of the position,
orientation and extension of earth barriers built to protect Rifugio
Sapienza, a touristic facility located near the summit of the volcano.
A preliminary release of the algorithm, called single barrier approach
(SBA), was initially considered. Subsequently, a second GA strategy, called
Evolutionary Greedy Strategy (EGS), was implemented by introducing multibarrier
protection measures in order to improve the e ciency of the nal
solution. Finally, a Coevolutionary Cooperative Strategy (CCS), has been
introduced where all barriers are encoded in the genotype and, because all
the constituents parts of the solution interact with the GA environment, a
mechanism of cooperation between individuals has been favored. Solutions
provided by CCS were extremely e cient and, in particular, the extent of
the barriers in terms of volume used to deviate the
ow thus avoiding that
the lava reaches the inhabited area was less than 72% respect to the EGS
3and 284% respect to the SBA. It is also worth to note that the best set of
interventions provided by CCS was approximately eighteen times more ef-
cient than the one applied to divert the lava
ow away from the facilities
during the 2001 Mt.Etna eruption.
Due to the highly intensive computational processes involved, General-
Purpose Computation with Graphics Processing Units (GPGPU) is applied
to accelerate both single and multiple simultaneous running of SCIARAfv2
model using CUDA (Compute Uni ed Device Architecture). Using four
di erent GPGPU devices, the study also illustrates several implementation
strategies to speedup the overall process and discusses some numerical results
obtained. Carried out experiments show that signi cant performance
improvements are achieved with a parallel speedup of 77.
Finally, to support the analysis phase of the results, an OpenGL and
Qt extensible system for the interactive visualization of lava
ows simulations
was also developed. The System showed that it can run the combined
rendering and simulations at interactive frame rate.
The study has produced extremely positive results and represents, to
our knowledge, the rst application of morphological evolution for lava flow
mitigation.; Università della CalabriaSoggetto
Algoritimi genetici; Computer graphics
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