Donato D'Ambrosio

A Cellular Automata model for soil erosion by water

Abstract A Cellular Automata model for soil erosion by water, SCAVATU, was developed. It involves a larger number of states in comparison to the previous models, including altitude, water depth, total head, vegetation density, infiltration, erosion, sediment transport and deposition. The model was applied to the small catchment of the Fiumara Armaconi, Calabria, Southern Italy. First simulations gave encouraging results, even if field erosion data is needed for validation and future calibration and setting of the CA parameters. The model is susceptible to improvement and could represent a valid alternative to classic physically based methods, for the description of complexity through simple local rules.

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”.

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.

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.

An Evolutionary approach for modelling lava flows through cellular automata

A Master-Slave Genetic Algorithm is applied to evolve a two-dimensional Cellular Automata model for lava flow simulation. The 2002 Etnean Linguaglossa case study is considered for model calibration. A quantitative measure for the evaluation of the simulations result with respect to the real event is defined and employed as fitness function.

The latest release of the lava flows simulation model SCIARA: first application to Mt Etna (Italy) and solution of the anisotropic flow direction problem on an ideal surface

Abstract This paper presents the latest developments of the deterministic Macroscopic Cellular Automata model SCIARA for simulating lava flows. A Bingham-like rheology has been introduced for the first time as part of the Minimization Algorithm of the Differences, which is applied for computing lava outflows from the generic cell towards its neighbours. The hexagonal cellular space adopted in the previous releases of the model for mitigating the anisotropic flow direction problem has been replaced by a–Moore neighbourhood–square one, nevertheless by producing an even better solution for the anisotropic effect. Furthermore, many improvements have been introduced concerning the important modelling aspect of lava cooling. The model has been tested with encouraging results by considering both a real case of study, the 2006 lava flows at Mt Etna (Italy), and an ideal surface, namely a 5°inclined plane, in order to evaluate the magnitude of the anisotropic effect. As a matter of fact, notwithstanding a preliminary calibration, the model demonstrated to be more accurate than its predecessors, providing the best results ever obtained on the simulation of the considered real case of study. Eventually, experiments performed on the inclined plane have pointed out how this release of SCIARA does not present the typical anisotropic problem of deterministic Cellular Automata models for fluids on ideal surfaces.

Cellular Automata and GPGPU: An Application to Lava Flow Modeling

This paper presents an efficient implementation of the SCIARA Cellular Automata computational model for simulating lava flows using the Compute Unified Device Architecture CUDA interface developed by NVIDIA and carried out on Graphical Processing Units GPU. GPUs are specifically designated for efficiently processing graphic data sets. However, they are also recently being exploited for achieving excellent computational results for applications non-directly connected with Computer Graphics. The authors show an implementation of SCIARA and present results referred to a Tesla GPU computing processor, a NVIDIA device specifically designed for High Performance Computing, and a Geforce GT 330M commodity graphic card. Their carried out experiments show that significant performance improvements are achieved, over a factor of 100, depending on the problem size and type of performed memory optimization. Experiments have confirmed the effectiveness and validity of adopting graphics hardware as an alternative to expensive hardware solutions, such as cluster or multi-core machines, for the implementation of Cellular Automata models.

Efficient application of GPGPU for lava flow hazard mapping

The individuation of areas that are more likely to be impacted by new events in volcanic regions is of fundamental relevance for mitigating possible consequences, both in terms of loss of human lives and material properties. For this purpose, the lava flow hazard maps are increasingly used to evaluate, for each point of a map, the probability of being impacted by a future lava event. Typically, these maps are computed by relying on an adequate knowledge about the volcano, assessed by an accurate analysis of its past behavior, together with the explicit simulation of thousands of hypothetical events, performed by a reliable computational model. In this paper, General-Purpose Computation with Graphics Processing Units (GPGPU) is applied, in conjunction with the SCIARA lava flow Cellular Automata model, to the process of building the lava invasion maps. Using different GPGPU devices, the paper illustrates some different implementation strategies and discusses numerical results obtained for a case study at Mt. Etna (Italy), Europe’s most active volcano.

Integrating geomorphology, statistic and numerical simulations for landslide invasion hazard scenarios mapping

This paper highlights a multidisciplinary method for evaluating debris-flow invasion hazard, based on geological-geomorphological field survey and statistical analysis coupled with numerical simulations through Cellular Automata. The developed hazard assessment methodology consists of different consolidated techniques for the (a) identification of spatial susceptibility, i.e. potential landslide sources in previous unfailed slopes, (b) estimation of the probability of cover thickness involvement in initial landsliding, (c) evaluation of temporal probability and (d) numerical modeling of potential landslides. In this study, the SCIDDICA Cellular Automata landslide model has been considered and applied to the northern sector of Mount Pendolo (Sorrento Peninsula), which was affected by several landslides in historical time. Model calibration has been performed by considering past events of similar scale and type. Results of these simulations were satisfactory as proven by the comparison between real and simulated reference events. Several possible source areas have been hypothesized and a potential future landslide scenario has been simulated by using SCIDDICA. By associating to each simulation a spatial, magnitude and temporal probability, a landslide invasion hazard scenario was mapped. We developed an integrated approach for debris flow invasion hazard scenarios.Spatial, magnitude and temporal probabilities and runout were assessed.The SCIDDICA model was able to well simulate the considered case studies.The validity of the approach is related to the quality and accuracy of input data.By varying statistical hypotheses, different hazard scenarios can be mapped.