Salvatore Di Gregorio

An empirical method for modelling and simulating some complex macroscopic phenomena by cellular automata

Abstract Novel parallel computing models sometime represent a valid alternative to standard differential equation methods in modelling complex phenomena. In particular, Cellular Automata (CA) provide such an alternative approach for some complex natural systems, whose behaviour can be described in terms of local interactions of their constituent parts. This paper illustrates an empirical method applied with interesting results in modelling and simulating some complex macroscopic phenomena. While classical CA are based upon elementary automata, with few states and a simple transition function, in order to deal with macroscopic phenomena it is often necessary to allow a large number of different states a more complicated transition. The notion of substate is introduced in the macroscopic case for decomposing the state of the cell. The values associated to substates can change in time either due to interactions among substates inside the cell (internal transformations) or to local interactions among neighbouring cells. The internal transformations are treated in a way similar to ordinary difference equations. The local interactions among cells can be often treated according to an algorithm for the minimisation of differences, which describes a tendency of conserved quantities to reach an equilibrium distribution. A large class of complex macroscopic phenomena seem to satisfy the applicability conditions of such an empirical method; some of them are briefly reviewed.

The simulation model SCIARA: the 1991 and 2001 lava flows at Mount Etna

Abstract Cellular Automata (CA), a paradigm of parallel computing, represent an alternative to differential equations for modelling and simulating very complex phenomena, whose evolution is based on local interactions of their constituent parts. A CA model for lava flow simulations, SCIARA (‘Simulation by Cellular Interactive Automata of the Rheology of Aetnean lava flows’) was developed and improved according to an empirical CA method for simulating macroscopic phenomena. Its last version, SCIARA-hex1, was applied to the 1991 lava flow of the 1991–1993 eruption of Etna. The simulation results are satisfying within limits to forecast the lava flow path. It was also applied during the eruption of Etna in the summer of 2001, when a new eruption threatened the town of Nicolosi. This ‘real time’ application proved that SCIARA is a reliable and flexible tool for forecasting lava flow paths and for assessing hazard in the Etnean area.

Predicting the impact of lava flows at Mount Etna, Italy

This work was sponsored by the Italian Ministry for Education, University and Research, FIRB project n° RBAU01RMZ4 “Lava flow simulations by Cellular Automata”, and by the National Civil Defence Department and INGV (National Institute of Geophysics and Volcanology), project V3_6/09 “V3_6 – Etna”.

Simulation of the 1992 Tessina landslide by a cellular automata model and future hazard scenarios

Abstract Cellular Automata are a powerful tool for modelling natural and artificial systems, which can be described in terms of local interactions of their constituent parts. Some types of landslides, such as debris/mud flows, match these requirements. The 1992 Tessina landslide has characteristics (slow mud flows) which make it appropriate for modelling by means of Cellular Automata, except for the initial phase of detachment, which is caused by a rotational movement that has no effect on the mud flow path. This paper presents the Cellular Automata approach for modelling slow mud/debris flows, the results of simulation of the 1992 Tessina landslide and future hazard scenarios based on the volumes of masses that could be mobilised in the future. They were obtained by adapting the Cellular Automata Model called SCIDDICA, which has been validated for very fast landslides. SCIDDICA was applied by modifying the general model to the peculiarities of the Tessina landslide. The simulations obtained by this initial model were satisfactory for forecasting the surface covered by mud. Calibration of the model, which was obtained from simulation of the 1992 event, was used for forecasting flow expansion during possible future reactivation. For this purpose two simulations concerning the collapse of about 1 million m 3 of material were tested. In one of these, the presence of a containment wall built in 1992 for the protection of the Tarcogna hamlet was inserted. The results obtained identified the conditions of high risk affecting the villages of Funes and Lamosano and show that this Cellular Automata approach can have a wide range of applications for different types of mud/debris flows.

First simulations of the Sarno debris flows through Cellular Automata modelling

Abstract Cellular Automata (CA) can be efficiently applied in the simulation of complex natural processes. They represent an alternative approach to classical methods based on the resolution of differential equations. In this paper, the general frame and the latest developments of the Cellular Automata model SCIDDICA (Simulation through Computational Innovative methods for the Detection of Debris flow path using Interactive Cellular Automata) for simulating debris-flow phenomena are presented. Landslides characterised by a dominant flow-type (e.g. earth flows, debris flows, debris avalanches) can be considered as dynamical systems, subdivided into elementary parts that evolve, exclusively, as a consequence of local interactions. In SCIDDICA, space and time are discrete: in particular, the space in which the phenomenon evolves is represented by square cells, whose states describe the considered physical characteristics; time is implicit in the steps of model computation. The peculiarities of the structure permitted to extend SCIDDICA first release, in order to progressively account for more complex phenomenological aspects of the considered landslides. In this paper, examples of application of SCIDDICA to three real landslide events are presented. After briefly describing earlier simulations of the 1992 Tessina (Italy) earth flow and of the 1984 Mt. Ontake (Japan) debris avalanche, first attempts at modelling a debris flow that occurred in 1998 at Sarno (Italy) are discussed. The model has been validated through the reconstruction of the initial topographic and geomorphological conditions of a selected, typical phenomenon (which occurred at Chiappe di Sarno–Curti, on May 1998), and by successively comparing the simulation results with the actually observed debris-flow path. Even though improvements to the algorithms are still needed, and further testing of parameters on a more representative sample of phenomena desirable, first simulations of the Curti landslide have demonstrated the reliability of SCIDDICA in the assessment of debris-flow susceptibility.

A New Algorithm for Simulating Wildfire Spread through Cellular Automata

Cell-based methods for simulating wildfires can be computationally more efficient than techniques based on the fire perimeter expansion. In spite of this, their success has been limited by the distortions that plague the simulated shapes. This article presents a novel algorithm for wildfire simulation through Cellular Automata (CA), which is able to effectively mitigate the problem of distorted fire shapes. Such a result is obtained allowing spread directions that are not constrained to the few angles imposed by the lattice of cells and the neighborhood size. The characteristics of the proposed algorithm are empirically investigated under homogeneous conditions through some comparisons with the outcomes of a typical CA-based simulator. Also, using two significant heterogeneous landscapes, a comparison with the vector-based simulator FARSITE is discussed. According to the results of this study, the proposed approach performs significantly better, in terms of accuracy, than the CA taken as reference. In addition, at a far less computational cost, it provides burned regions that are equivalent, for practical purposes, to those given by FARSITE.

SCIARA γ2: An improved cellular automata model for lava flows and applications to the 2002 Etnean crisis

Abstract Cellular automata are widely utilized for modelling and simulating complex dynamical systems whose evolution depends on the local interactions of their constituent parts. Simulation by Cellular Interactive Automata of the Rheology of Aetnean lava flows (SCIARA) is a Cellular Automata model for simulating lava flows; its release γ 2 introduces innovations to the empirical method for modelling macroscopic phenomena that was utilized in the previous releases. The lava flows are described as “blocks”, individuated by their barycentre co-ordinates and velocities. This approach is different from the previous releases of SCIARA and from cellular automata derived models for fluid-dynamical phenomena such as lattice-gas and lattice-Boltzmann models. Block specifications permit to obtain a more physical description of the phenomenon and a more accurate control of its development. SCIARA γ 2 was applied to the 2002 Etnean lava flows with satisfying results, obtaining better simulations in comparison with the previous releases.

Accelerating wildfire susceptibility mapping through GPGPU

In the field of wildfire risk management the so-called burn probability maps (BPMs) are increasingly used with the aim of estimating the probability of each point of a landscape to be burned under certain environmental conditions. Such BPMs are usually computed through the explicit simulation of thousands of fires using fast and accurate models. However, even adopting the most optimized algorithms, the building of simulation-based BPMs for large areas results in a highly intensive computational process that makes mandatory the use of high performance computing. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with a wildfire simulation model based on the Cellular Automata approach, to the process of BPM building. Using three different GPGPU devices, the paper illustrates several implementation strategies to speedup the overall mapping process and discusses some numerical results obtained on a real landscape.

Pyroclastic flows modelling using cellular automata

Cellular automata (CA) and derived computational paradigms represent an alternative approach to differential equations to model and simulating complex fluid dynamical systems, whose evolution depends on the local interactions of their constituent parts. A new notion of CA was developed according to an empirical method for modelling macroscopic phenomena; its application to PYR, a CA model for simulating pyroclastic flows, generated PYR2, which permitted an improvement of the model and a more efficient implementation. PYR2 was utilised for the 1991 eruption of Mt. Pinatubo in the Philippines islands and for the 1996 eruption of the Soufriere Hills in the Montserrat Island. Results of the simulations are satisfactory if the comparison between real and simulated event is performed, considering the area involved by the event and the variations of thickness of the deposit, as generated by collapsing volcanic columns.

SCIDDICA-SS3: a new version of cellular automata model for simulating fast moving landslides

Cellular Automata (CA) are discrete and parallel computational models useful for simulating dynamic systems that evolve on the basis on local interactions. Some natural events, such as some types of landslides, fall into this type of phenomena and lend themselves well to be simulated with this approach. This paper describes the latest version of the SCIDDICA CA family models, specifically developed to simulate debris-flows type landslides. The latest model of the family, named SCIDDICA-SS3, inherits all the features of its predecessor, SCIDDICA-SS2, with the addition of a particular strategy to manage momentum. The introduction of the latter permits a better approximation of inertial effects that characterize some rapid debris flows. First simulations attempts of real landslides with SCIDDICA-SS3 have produced quite satisfactory results, comparable with the previous model.