William Spataro

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.

A parallel cellular tool for interactive modeling and simulation

The paper discusses Camel, an interactive parallel programming environment based on cellular automata. With Camel users can develop high-performance applications in science and engineering. Examples in geology, traffic planning, image processing, and genetic algorithms show its usefulness.

A parallel cellular automata environment on multicomputers for computational science

This paper describes CAMEL (Cellular Automata environMent for systEms modeLing), a scalable software environment based on the cellular automata theory implemented on a Transputer-based parallel computer. Cellular automata were originally defined as a theory to model the basic mechanisms of dynamic systems, permitting a new approach which is in many cases simpler and more efficient than the traditional approach based on partial differential equations. Today, cellular automata become more attractive because they are suitable to be effectively and naturally implemented on parallel computers achieving high performance. CAMEL allows a user to program computational science applications exploiting the computing power offered by highly parallel computers in a transparent way. CAMEL implements a cellular automaton as a SPMD program. A load balancing strategy is used to minimize time costs in case of not uniform intervals for transition steps. In the paper the programming environment and the parallel architecture of CAMEL are presented and some experiments are discussed.

Mount ontake landslide simulation by the Cellular Automata model SCIDDICA-3

Abstract Cellular Automata (CA), a paradigm of parallel computing, represent an alternative to differential equations and are used for modelling and simulating very complex phenomena; CA models have been developed by our research group for the simulation of landslides. We present SCIDDICA-3, our most efficient model, a two-dimensional CA model together with the simulation results of the Mount Ontake (Japan) debris avalanche which occurred in 1984. Landslides are viewed as a dynamic system based exclusively on local interactions with discrete time and space, where space is represented by square cells, whose specifications (states) describe physical and chemical characteristics (friction, viscosity, altitude, debris thickness, etc.) of the corresponding portion of space. At the time t=0, cells are in states which describe initial conditions; the CA evolves then changing the state of all cells simultaneously at discrete times. Input for each cell is given by the states in the adjacent cells; the outflow computation from the cells gives the evolution of the phenomenon. The comparison between the real and simulated event is satisfying within limits to forecast the surface covered by debris.

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.

Parallel genetic algorithms for optimising cellular automata models of natural complex phenomena : An application to debris flows

Abstract Cellular automata models of natural complex phenomena may depend on a set of parameters which can significantly influence the global dynamics of the simulated events. In order to reliably apply such models for predictive purposes, their parameters have to be estimated with the greatest possible accuracy. However, no standardised optimisation techniques exist in this specific research field. Genetic Algorithms (GAs) offer a possible solution: they are parallel algorithms, and can be easily implemented to exploit the simultaneous use of multiple CPUs, thereby greatly reducing the execution time. An application of a parallel GA to the optimisation of a cellular automata model for the simulation of debris flows characterised by strong inertial effects is presented. The May 1998, Curti-Sarno (Italy) debris flow has been selected as a case study for the optimisation of the model. Theoretical considerations on the dynamics of the adopted GA are discussed, with reference to two different fitness functions applied to an idealised case study. Results demonstrated the usefulness of the approach, in terms of both computing time and quality of performed simulations. Moreover, experiments on the idealised case study pointed out that the simplest fitness function (only based on the comparison of affected areas) could conveniently be adopted for calibration purposes.

Revisiting the 1669 Etnean eruptive crisis using a cellular automata model and implications for volcanic hazard in the Catania area

Abstract Cellular Automata provide an alternative approach to standard numerical methods for modelling some complex natural systems, the behaviour of which can be described in terms of local interactions of their constituent parts. SCIARA is a 2-D Cellular Automata model which simulates lava flows. It was tested on, validated by, and improved on several Etnean lava events such as the 1986–1987 eruption and the first and last phase of the 1991–1993 event. With respect to forecasting the surface covered by the lava flows, the best results were acceptable. The model has been used to determine hazard zones in the inhabited areas of Nicolosi, Pedara, S. Alfio and Zafferana (Sicily, Italy). The main goal of the current work in the Etnean area from Nicolosi to Catania has been the verification of the volcanic hazard effects of an eruptive crisis similar to the event that occurred in 1669. The simulation uses the volcanic data of the 1669 eruption with present-day morphology. Catania has been affected by some historical Etnean events, the most famous one being the 1669 eruption, involving 1 km 3 of lava erupted over the course of 120 days. The simulation of ephemeral vents and the use of different histories within the experiments have been crucial in the determination of a new hazard area for Catania. In fact, during the simulation the city was never affected without the introduction of ephemeral vents, proving the fact that lava tubes played a fundamental role in the 1669 Catania lava crisis.

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.

Parallel evolutionary modelling of geological processes

This paper illustrates a parallel computing methodology for modelling and simulating geological processes by means of Cellular Automata and Parallel Genetic Algorithms. Two models, concerning lava and debris flows, have been implemented using the Cellular Automata development environment CAMELot, and calibrated by means of Genetic Algorithms through the PGAPack library. Experiments have been carried out on two different distributed memory machines, namely a Beowulf cluster and a HP Alphaserver SC supercomputer. Results have demonstrated the goodness of both considered geological models and of the genetic algorithm employed for their calibration. High computational performances have been achieved. In particular, results obtained for the calibration phase demonstrated that even low-cost parallel machines can be fruitfully employed for the construction of reliable simulation models for geological processes.